{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "28eb4ab209ed1421",
   "metadata": {},
   "source": "https://tushare.pro/register?reg=804648"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-30T14:14:50.498740Z",
     "start_time": "2025-06-30T14:14:50.496457Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# import numpy as np\n",
    "!pip install tushare"
   ],
   "id": "11b47f91e0532597",
   "outputs": [],
   "execution_count": 2
  },
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-30T14:14:50.552669Z",
     "start_time": "2025-06-30T14:14:50.550127Z"
    }
   },
   "source": [
    "# # 获取A股历史股价数据\n",
    "# import tushare as ts\n",
    "#\n",
    "# # 设置token，获取token请自行注册tushare\n",
    "# ts.set_token('用自己的')\n",
    "#\n",
    "# pro = ts.pro_api()\n",
    "#\n",
    "# df = pro.daily(ts_code='600030.SH', start_date='20180101', end_date='20191231')\n",
    "#\n",
    "# df.to_csv('600030.csv', index=False)"
   ],
   "outputs": [],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-30T14:14:50.934930Z",
     "start_time": "2025-06-30T14:14:50.575950Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import pandas as pd\n",
    "\n",
    "# numpy、pytorch、matplotlib\n",
    "\n",
    "# 将trade_date字段转成日期格式，并设置为索引字段\n",
    "df = pd.read_csv('600030.csv', parse_dates=['trade_date'], index_col='trade_date')"
   ],
   "id": "9f9bf19a2a19ea6f",
   "outputs": [],
   "execution_count": 4
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-30T14:14:50.954900Z",
     "start_time": "2025-06-30T14:14:50.951259Z"
    }
   },
   "cell_type": "code",
   "source": "df.index",
   "id": "f8023e6581ab9acf",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2019-12-31', '2019-12-30', '2019-12-27', '2019-12-26',\n",
       "               '2019-12-25', '2019-12-24', '2019-12-23', '2019-12-20',\n",
       "               '2019-12-19', '2019-12-18',\n",
       "               ...\n",
       "               '2018-01-15', '2018-01-12', '2018-01-11', '2018-01-10',\n",
       "               '2018-01-09', '2018-01-08', '2018-01-05', '2018-01-04',\n",
       "               '2018-01-03', '2018-01-02'],\n",
       "              dtype='datetime64[ns]', name='trade_date', length=476, freq=None)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 5
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-30T14:14:51.045791Z",
     "start_time": "2025-06-30T14:14:51.040769Z"
    }
   },
   "cell_type": "code",
   "source": "df.sort_index(ascending=True, inplace=True)  # 本地修改",
   "id": "7e94a6fcce17d6c1",
   "outputs": [],
   "execution_count": 6
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-30T14:14:51.073113Z",
     "start_time": "2025-06-30T14:14:51.069570Z"
    }
   },
   "cell_type": "code",
   "source": "df.index",
   "id": "4fed36461748719a",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-01-02', '2018-01-03', '2018-01-04', '2018-01-05',\n",
       "               '2018-01-08', '2018-01-09', '2018-01-10', '2018-01-11',\n",
       "               '2018-01-12', '2018-01-15',\n",
       "               ...\n",
       "               '2019-12-18', '2019-12-19', '2019-12-20', '2019-12-23',\n",
       "               '2019-12-24', '2019-12-25', '2019-12-26', '2019-12-27',\n",
       "               '2019-12-30', '2019-12-31'],\n",
       "              dtype='datetime64[ns]', name='trade_date', length=476, freq=None)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 7
  },
  {
   "cell_type": "code",
   "id": "7d517e8dbc15d6a1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-30T14:14:51.111621Z",
     "start_time": "2025-06-30T14:14:51.108092Z"
    }
   },
   "source": [
    "# 收盘价\n",
    "df['close']"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "trade_date\n",
       "2018-01-02    18.44\n",
       "2018-01-03    18.61\n",
       "2018-01-04    18.67\n",
       "2018-01-05    18.88\n",
       "2018-01-08    19.54\n",
       "              ...  \n",
       "2019-12-25    23.13\n",
       "2019-12-26    23.75\n",
       "2019-12-27    23.33\n",
       "2019-12-30    25.66\n",
       "2019-12-31    25.30\n",
       "Name: close, Length: 476, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 8
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-30T14:14:51.549643Z",
     "start_time": "2025-06-30T14:14:51.147082Z"
    }
   },
   "cell_type": "code",
   "source": [
    "data = df['close']\n",
    "data.plot()"
   ],
   "id": "4674488a94dfe512",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='trade_date'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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PVx/ftGkTPf/887R+/Xrq3LmzNxcBAADaLXISHxVKd57RU1jYR4eF0Deawko+2MkTrgRFnJRU1pI0HHVHnCzbZfXd4Jk6/PpOMeF2xaVsEqZXtGkdKdj07AqbFG3tqJJwTRB/zywAAzFyUqOzgth2rTkpLbVWniclWfvHmcrKSrr88svplVdeobS0tBbfo6amRlwkZWWB6d4HAPBjzYnmrPq+aX3UdI6jOJHIyElFbYMaRWhJnPSyi5wEq+ZgWnGi14NmaWWdmMBsiMiJMoU4UWn31hKuiJOAjJzUmzito6WxsZFmzZpFY8eOpYEDB6qP33PPPTRmzBg6//zz3a5jiY+PVy+ZmZnttcgAANBs5ESLtkaB6RhjO9jFKeJEe6beXCuxs7QOkxZnn3rQY+SE6wcXbM63K/qta7DYzR/SE3IKcVK07TuSaDt2Ao2aehMXxGrh2pOtW7fSxx9/rD729ddf088//yzqTdxl9uzZIgIjL3l5ee20xAAAYMu9a3FWa8IHYm0wRBbDyihJbESI3Zl6UAu/tj2dRU6Ujh2JHg+aq/YV0V/nb22yDnpcVq17rbPIiTwwmzlyYrFYnLZKqw6xZo6c3HHHHbRw4UJasmQJZWRkqI+zMNm7dy8lJCRQSEiIuDAXXXQRTZw40el7hYeHU1xcnN0FAAD80UqshetLuMtG4lhgKVM7xRXWtHRIC+okISpMjb64Eic1OqzlkHUyTLZiw69ncWKLnDhL6wTr9nP2BpW19TT+n0vopvc36N4h1qs1J6zG7rzzTpo3bx4tXbqUunfvbvf8Qw89RDfccIPdY4MGDaI5c+bQueee681FAQCAViEPqjylVrYIy24dRyLDgumk4o/hTJwcOlFFRcqZenMmbNqDe+HJYvUMngti9Z7WyUi0zhGSnx1HmWobGtXWVN1GTpyIE/m5V+u4ZqYt7DxaLlq++bKroJx6p9o8c6QgM2W3DqdyPvroI/rqq6+E18nRo0fF41wrEhkZKQpgnRXBdu3atYmQAQAAf0ZO+MxaihNnkRMmOiyYZNwgWVNzorWwl2fq7nh69UqNoTW5xaITSPyNg6DRYzRC2948sU8nWru/WIgTZ+kxf8MDG2XhblxEaMBFTmo139XHa/Pob+f2V+9LkW3KtM5rr70m6kI4RcMtwvLyySefePO/AQCAdkMKAO2ZtStxEqlJ62iLYLVpnSJlYnFwS0UnRHTNmCw6b0g6XXyKNR0+bUAqZWvcY/UYOZGfF0dMrhjVTT246bGduFLz+cWEhzRTc6K/z9lbaR3JR2sPiPEKzCfrDtLnGw7pqiDW62kdX/wNAAC0e+REUzDprCBWRk4knAZyJk7UyIkbv/k9U2LpX5cNU+/HRoTST/dNpL8v3E5vr8zVzQG/XjFvCQkOoqpa64H88lFdReqqudQI/96/+8t+6t4pmib1SfHxUpNqUc8BKWcHYbNHTiqV74rhbYndck/tnkR//mKL+rgpIycAAGB0ZCQgyY3ISZTm7Dsm3D5ywsJCTiZ2x+ekOdQDvg4iJ5wamfHvlTTxuaW0u6BcjebIIt7m2nF/P1RKjy/cTn/+fDP5AylOosNDnDqUm73mpLLGfr32FzZ1b9dLQaw+lgIAAHRYc9JS5CRK43UiW4clEcqPvHy/5mbrtAQ7xjqG5f0F12xwYSUX+17yxipat79YPB6pihPrem873NQwc21ukTp9uU5a5/qQCuXg7Cylo40asKmcGaP6lQ7bz/4iJ+JEJ2kdfSwFAAAYsOYkWFPlGuMgTsIdTNqam0rcEtwVxHy6/pDosvAn2rqXE5V1YsIvExlmP3/oyW93UJ7G0p5Zt/+EervwpO8HGp7URE6cIYXV84t30cNfWb1bzESFJq3DHCiy/34YpHUAAMAgNSeuWok5xSGJ0RTHOguPu9NK7AqtOHp7RS7pQZywCNEGg2RxMAsWyUdrD6q3ORKx4YBNnPhj2rKa1tHUCrk6MH+w2rbsZkHWB2UmWSdg5zpJ6+ilIFYfSwEAAHrzOYlsuV9Aa9HuKD4cxUlL9vXNwSkUSYg7Pck+SA1wq3RPjemaTOvkl9iWdcVum0HbvsIK1WOEOVbmB3FS23zkxFW6xyxUKOvfL81qZnqgqKJJ+gqREwAA0HHkhN1Zp/VPpTP6ptjNzdHS2ExdguOPfFsKYs8aaPOHOlJqGwToT/HGqabBGQlNxInWYZe7QWRb7nqlNkVyXIdpnaGZtvXRRlrMFjnpmxYrOpY4zVOotLpLUBALAAA6RLbrctfJm1eNoHeuGem0s4NpbridY2FhW8TJsK6J9OxFg8VtxzoOX1NV26iKkewUmweLrDnRwgMAWaA41ps4i5xwgezhkipxJr9k5zF6ZckerxelSrHhKkIysnuS/TL6IfXki5qT+KgwSk+IdFoUi8gJAADoEOls6s4ZZEMzx84maZ02iBOG/SiYvBOVfu0kkWkdjpx0TbJZ18sW4jeuPIW6JUep3UuyYFbWmwxIj7Pzf2F4fc57+Rca+8zP9NiC7XTt3HX0z+9zaKlmbo83OKl060SHOz8AszfNtWOz1PsFZf6NUnmbSk3NTVZytNN2YlfF375GH0sBAAA6QaYl5MG2OSb3tRqJJTczRE7SllZihs90Wd9wZMcfKRHHgliOnGjFiUzrnDkgjZY9MImuGt1N3N96uJTKq+vU4ktpvlaiESf5pdW044i19fh/miLavcdOtpvPiSseOXcAjVKEoNkiJ5VK5ISFJQtIZ5GT+kZ9GNCZu/oHAAA8gNM0PBfGXXFyxaiuYqbOiG726QBnkZO2tBLLM9rO8ZEi9cGpnZRY+4nFPq85CQ2mTM3QP8fU16Au8Wrk5KCSimIR11U5KErrdGbbYWt0xbFmRc428npax6GzytkwQ55xtO+4d8WRvylRPnNOa3XvaI2c7C6wX8dOsfbDJv0FIicAAOBkMJo7aR22b58xOJ3S4iPateZEIltAeaqsv4sq+exbDjd0dlAbpBTLbj9SRpvySsTtjKQoSlBs/eWBktmW39SwjTnk5foaWXPRXOSEGdglTo36mIXK2nrh6Mv0SYulbkpahw31JO9dd6rfRK8jiJwAAICC1nK9rV0LTdI63hAniVG0morVSIQ/h+dx5ISjJd/dfbpoEe6iFFhK0uMjhMsuP/d/86yGZpwGSlD8Y9iFVcJ1NLIr6but1mn2rhxMvZPWaT4qNjjDFvUxC5vySqi+0UKd4yPEdyW3dbktsXg+vVdH0guInAAAgENKgVMwHBVpC45mVm3xOZHIGg9/duxUayInTL/OcTS2Z9ODGgsXWVwsyUyMVKMt7Hty9ksr6GBRJZVVWUXD6b062dWxeLvmo6VWYkn/zvGivof/f7MUxW5QuqVO6ZYovhtOXWk3SU5puupK8wcQJwAAoCDPJt2pN2kJb/qcSDKVA7c/IydqQawLl1Utt03q2aRVV6Z1ZMpnweZ8KquuU7tlLhjWRX3+hMa0rTmOllbTYwu2OXU89bQgVq5br5RYcXuLSaIn65RuqZFZSeo2nh5vH+3SE0jrAACAQ+TEG0ZU3m4l1ooTrWOs3zo+3BBwN5zeXRRespEdRy06xoQ3mVbMdR1lSv0Ju/LeeUZPykiMpAc+3yxqRPj1LYnFuz/eKApYF24+Quv+b0qrfU60DMqIp5yCctp8uJSm9E8ld/llTyGt2ltE90zt7ZXv3BtwVOQ3RZyMyEpUHw/VuA33TrW5/eoBRE4AAMAwkRPrmS537Oz1UyeJtpXYnc/g7EGdxefJwoTh29q/3ZpvEyccOeF02sWnZKjdTVo/FFf8dvCEW/N6VJ+TFrp1tHUnLJ64mPTxBdubuNw6wh1It3ywgV5esofW7LNOYNYDOUfLhThkUdZXsa53HAT41lUjSU9AnAAAQHtETtqhW6eTcoBnLntzNfmDGo2DbmthESLhziP2OWHiIqyPc+2DnApd5GCv7gw5CbklPImcDNS0Qv9neS6980suXfz6qmb/5t1fcqlcaX8ucjMl5QvWH7CKqmFdE+y2w6sVL5oJvTupLd56AWkdAABwiJyEeyFy4njA9EZBLB+0uWWXIwT+Mghjm/m2DiDkotijTgpN4zSihT1ReD21wwKb84DRRgFcpTZk1Kelbh2mf+c4cSAvPFlD763a3+LrOWryzkrbxGhZR6MHfs8rVYthtdw4vgf1TImh0T3006UjQeSkjXB49fttR/1qJw0A0F/kxLF12ButxMynN492O63SHsh5Qm0xldOKELvHFct7JlFpOXYnreOO5bqcyOtOQayMDEmrfW0UxLFmRvLB6gN2pnGyA0kPnKyxCiWZWtOm3aYP7EzxGr8avQBx0kbGP7uEbn5/Ay3YfMTfiwIA8FrNifd/Gr11ApOkHLQ5CqA1jfMV7JXBBAe1/jNyJv543ou2fZs9Uhh3IiehbqR1ZEqHoyHuis9/XjykyWOHFE8WRzYeLLHbdvQUOanxouj2FcZZUp0izyKW5Xh3QBUAQN9zddzhrsm91NsHirzT/ssD9WSGyB8HQPmbp+308BRn9TeO0RRZs+OOANNGThpdTIquUIthreZx7sBOqiMcUiGu2rir6qzih0cMMLLIVw/UKHVC3khX+gqIEy8h53EAAExQc+KlM8x7p/ZWb9fUN18T4S6cHpIFndr5NL5CDoZrS4FviJOoixxEJwlVXiMjNe7W98iCVEekmVqsUnTrLqdoWm+bGx0gxU9aXES7zAVqC3LbQ+QkAKn10g8PAMAcrcSSf182jIZkJtCdZ9iiKG1Fdrv4RZw0tL3mxNnfnjM43f41SmRGFuA2u0waASM/k/2FFTT7y83imvlmizX1Pjo72aNl5cJYdyIn3G7MyDlLuoqc1BsvrYNuHS/hj9wvAED/P+LnDkkXF29ibbmt8ssB0Bs1J8GalNBHN44S3SSXn9rVaR2JO+JEDiNkthwuFW2x1/13He07XkG/7i2iZQ9MohW7ran3GYM7e7SsnNrR4mp0gDSnU8VJK1JuvB4skBOVehvvb9dI6wQcSOsAYHxq2iFy0h74M3Kiduu0oeZEdsEwY7I70q0Ts5ukiWRNi4zUNIeMWjC3f/QbfbfliBAmstaHn5fpmMHKtGR36dExxs3IiXXb4cF6zaWXmmPKC8to2N8X2w1F9Gpapx0KvdsLRE68BCInABgfo4S/pTjxZ+SkLWmd68d1F1GCSX1TXL5Gdu7UuSFOpH+J5M9fbLa7v+eY1U23Y0yY2gXkLlxs++oVw4Ut/YdrDorRAdx55VhUKwUSW+8zBaXVTl/nCo4QsTUFs+lQiTBG83pBrM63ay3GWVKdA3ECgPFpj5qT9oBn0Pir6LK+oe0FsZxeuG9aHxre1b7YVEuo8v6yANcV3J1TrRx8JY6fy+4Cqzhhw7HWwBb8D8/oL26zDfwJh8hGg2YZBqbHiwLd8hpbtKYlWKiNeeZnu3XyJkjrBDDyywcAGBejRU78mtZpQ82JO9giJ41uR03u1rRua9l1rFxcy0nDrYEFa2pcuNO6E+0ycEt07zSrCNqW795E42W7jtnNBSr2svU9unUCGNScAGB8DBM5idBBWqcNNSfuYOvWsXgkTiY7SRXtUSInbZ2821WZCu1Yd1KpGLxxsIcFwCBlLs9CpUOoJRxTP0UV3htNwKklVXQbqObEOEuqc5DWAcD4GCZyEqWHyEn7ihPpXSLTSC116rAzK3vA9O0c6zJy0rMNkRMmM9G5OJFzfaLD2CCvA10xqpswyvtm8xE65mSGUHMFve4OO3QXFnfSnBhpnQAEaR0AjI83B/+ZNa0j0yzemLLcHFL81DW6FzmJCrPW4fRNs/clYWTtR1sjJz06RYvrt1bsoyU7jzURF5FhwepEYzlmoNiN2UCO3TmFXhQnWvM/vYtuLcZZUp3jTi8+AEDfGCVyoqZ1qgOg5qSFEz/ZwisHIfZ18CWRcJdOssPgO0+5fFQ3GtglThTEXjt3Hf1j0U4R2ZHLoB0oGKVMPpbOsc1R4iAyC0/WtMuJs963ay3GWVKdg7QOAMZHemq4M0jOn8g5NKUmrjlRfU5aiJw4Ri26d4y2s7OXtLZTx1HgfHHrGLpqdDdx/7Wle+ny/6yhfcdPNpkUHa1EchxTNszR0mq7767EIXJy0IWXirs1Jje9t54uePUX0VkkxQm3RLvb1qwH9L0HGghWzktzbGE+AIDxaFCS80E6/xGPV1qJvW3WpaeaE3cdYmUqLkoRJxxxcSZE2prSkXDdxuPnD6SXLx8mZhyt3V9Mf/5ii3guPcFqwKaNojhGTrgr54znl9Klb6xSH5NC5cbTu4vr/UUVTkWNO+QUlNMP2wvElOR//7xbNRY0UtSEMdbS6gzHEehvLt/nt2UBAHjvwNve9RTeipywl4a3PTF8MfjPk7ROSw6xMqWi7bByVhTbO7VtxbCOzBicTgvuHEf9NLN3enSyCSApliqUTh7Jyj3HxTKzMZw8hkhxwqIqOTpMFLBKbxZPWbytQL397sr9tKug3HDFsAzEiRd+yCRrcoupyIu5QgCAOaMC3qo54YMYCxT/DP5r38OHNGFrKXIixYkUA67qTib1ce1G21o4hfR/Z/dT7/foaC2YZaJdpHV+3VOkpqs45cI1K9I3JT4yTBVWO4+WtWqZcoustv3S4mLWJ5vEbUROAghtLjQrOUr8sC3eblOtAABjihNuSdUzHCWQBxtfep3wmb5t8J9vIictdes4pnWcdexwEWum4lHibYZ3s83qSU+wWtfbpXU0QwmZVfus4kTO35n95RbafeykqJPh5eyTal32nUetEQ9PqVTSSNeMyRK+K9K5VlrjGwWIEy9FTs5RJl1+t/WoH5cIANAWGpUwe7DOa0781U6s1QmyYLW9sA3+a/Q4rTOsa4IYwDelXyp9c9c4+vCG09ptObmF+a/n9KNLRmTQ2J4d1cej1W4dW+SEIyQ8m0fyypI99NmGQ0Lo/euyYZSRGKVGTnIcxAmn7zhF01Iar0KJ1AzOiKebxmerjw/N9Gzgob/B4D8vFM/J/OMrS/bSr3sLxY+F/OEAABgxckK6h39jjpXX+DRyop1zE+yjgtiWak6kCZs2chIbEUorHpwkltEXHSo3nN6jyWPSd0UWxHLUiY8PWmSk/a4zetH0gWl2KamdR8vtBge+unQPPffDLrptYjY9OL2vG2muEPrz9D40uV8K/XbgBI3OTiYj4dVd8Omnn6aRI0dSbGwspaSk0MyZMyknJ0d9vri4mO68807q06cPRUZGUteuXemuu+6i0lL35g/ojQbNTtMnNZZ6pcQIN74Vu4/7dbkAAG074TBC5EQWxfrS60QrFNrd58TNmhNHEzb174P92zobo0ROuObk4flbadw/ljSJrBco7rHdFXM3Of8nqIN1vs5xTQ0jCxPm1aV7m/1/ZaSGIze8/iOzkujmCdk0OMNYkROvbl3Lli2j22+/nVavXk2LFy+muro6mjZtGlVUWAt08vPzxeW5556jrVu30ty5c2nRokV0/fXXk9FrTqy2ydZc4bEyFMUCYEQaDdKt46+0jvY3z3c1Jy2ldep1OQ9JjZzUNtD7qw+Imo+lOcftPjv5eUohI/1aspKtYmVRK8oEtJETI+PVpWehoYXFB0dQNmzYQOPHj6eBAwfSF198oT6fnZ1NTz75JP3pT3+i+vp6CgkJMWR+Wip8uYGx8Q0AwMA+JwYQJ3ERIT4XJ9o6O9/N1mkprdPYJK2jB2TNSYkT+3q2t/89r8T2Wgch0S05ivYVVtDfvtpG43t1oixNF1BLSLEm/3+j0q5xOZmuSUpKavY1cXFxLoVJTU0NlZWV2V30gmPVOhvyOOtrBwAYK1VrhLSOjJyUVdX7vOaEf/LaW8C5P5VYcYjVWeSE24KZzYfsyxbYx6STg41+jCI0JSO7246ZWw57VvYga1yiDR45aTdx0tjYSLNmzaKxY8eKiIkzCgsL6e9//zvddNNNzdaxxMfHq5fMzEzS2w+ZPIOQrWOInABg8JoTA0RO/JLW8ZHHibZbp8WaEzlbR2eRk9N7daTEqNAm3w+3G2vTONoTW8mVp1nt8Zm8E5V2hp88fbm5yJatBkdfn4entNsWxrUnXFfy8ccfO32eIyDnnHMO9e/fnx599FGX7zN79mwRXZGXvLw80gvqWYRD5ATiBABjIo+DRhAn/iiI9aWDrhRA7rYS6+1gzCerN45v2sXDFvfaAYHytVpiI0Lprsm9xO1nF+XQjztso1Gac3qVwsTZexqNdhEnd9xxBy1cuJCWLFlCGRkZTZ4vLy+n6dOni66eefPmUWio67bb8PBwkfbRXvRac2KbpQBxAoChfU4MJE78URDb3kP/mNAQ90zY5AFZb2kd5qrRWZQQZX9865IQ1SSN4xg5YdLjbXN6bnxvvXq7pt71lOP3Vx1QbxvNEdYRry49h55YmLDg+Pnnn6l7d+sQI8eICXfwhIWF0ddff00REbYvwGjYak6CXKZ1Nh8qafUAJwCAn3xODFBzIi3sfVsQ2+gze39pX19v0LSOFB13TOpp91iPTtHUXzOPh4WwMyHRy8UsIHZ8dZXq+seineptI00gbndxwqmcDz74gD766CMRFTl69Ki4VFVV2QkTbi1+++23xX35moYG12pQr8j8q5zOHeswhXL+xsN03su/0IOfb/bfQgIATN1K7FsTNvsTMl+0EvN/6TjHzEits9eP606zz7KZps0Y3Jkm9O6k3ud1cyYkTumWKF7rjJPVTU94zRax9+oW9tprr4m6kIkTJ1Lnzp3VyyeffCKe/+2332jNmjW0ZcsW6tmzp91r9FRL4vmQMOeREzbeYRZuPuK3ZQQAmNOEzVYQ68NuHYcmgPZEmzpqrihWz2kdhoUHC5Q/jsyk5/4whBKiwsRFtko3x91K3QkztX+quo48k8eR/ZqBf1yMa3S8KjW1FcXOYNHS0muMXNkv+8pZnPB6+npaKAAgcOzr4yJD1MiJ1ua8PfHV0D9Ge/BmceLKZM2Zfb3e4CjQMxcNtntsbM9kWqKYsrkiIzGK+GvlQ81Vo7vRtsOlVFXaQIdOVFLXZPtBhvsLrZONU+PC6dUrhpPRMcAuaJzx6lqfk73HbSq2d2qMn5YQAKDXbhRvRU5qGxqppr75ugxv15y099A/huswZNssW7m7SsPJyIneHGJb4okLBlFKbDjdMsE2nM+RyLBg+stZ/ejWidk0NrsjnabMx/lp5zEhSLVDAHccsXqAnd6rk+j2MTr6TNIZBBnidGwl5hzo0hxb65cvPAEAAIHlcxIdFiLM0Pj4xEWxvjg42+rs2v/z4UhQSmwEHSyupOPlNdRNsXTXohVleo6cOKNLQiSt+cvkFiNeN2rakaf2S6UvfztMK3cXilrGH7YX0MI7x1FmUpQ6022UxsDNyOCo6cXIiWztYxZo6ky0kzwBAPqEz0Rl1tkINSd8UuTrdmK1ldhHJ1ydYq1Oqjx92RnaTki91pw0h6epuEEZ8eJ617Fy+mzDIfG9f7vlCJ2oqKXNipPseE2xrZFB5MSLZ1k84pujJ1xzop2b0JL9MgDA/2g7QowQOZGpnZLKOp917Piy5oThtAdzTJne66pTh1NARpiH1FbS4yNFhEiuN8ORFG4v5sNRn9RYSo0zrj2HFogTL/f8s+GOo0NsS/bLAAD9nGwwRjnQ+drrpEap7whvxkK9XcSJi8hJtUms2t0lKKgD9UqJod8PlQpBxmmtnIJycWHG9zZ+l44EaR0v1pwwiVHWYU/MNWOy7F4HANAv2uyrEdI6dl4nPrKwV4tPm7FQ9yYpShTAdVpH3x4n7cHobKsAefS8AU2eM0tKh4E48ZDt+WV0yRuraM2+oib29Y4Kflr/VHGNmhMAjBU5MUpaR7YTl1b6KnLS2OLwOd/WnMhOncA5lN03rTcte2AiXXZq1yadoCOzzFEMywTON+olHv5qK63NLaZL31ztNP8qw4xMSpx1x0LNCQDGqjkxgn29P4zYqut927Yr0zrcreNYvMwzZmxpncCJnIQGB6mdS6/96RTq0dF6e+bQdMO1UzdH4HyjXkJrG+zoEMtUaAqV3J2qCQDQlzjxhQOqEScTSzHgq4OgjJwcL7cviL3vs99p8bYCumdqb8N26niD7E4x9PP9Eym/pIqSom0lBWYAkRMPyepoc+U7dKKqSc2JdCv0ZKomAEBnkROjiBMfF8RyV4hvIyfWmpOiilq7kzzuUGEH7leX7hX3E6ONbzrWFtITIk0VNWEgTjykA9l+tP75fU6Ts6wHp/cR11eP7ub2VE0AgP+RNWRGqTexT+v4OnLim0NHcnSY+D74qyk8WdskSlR40pru6aeZ8gvMAdI6HuLYJuz4Y3b+0C40vGuicP8rUX4w5FRNI/3oARCw1vUGqTfxx2RiX0dOOILVMSaMCspq6Fh5NaXFR9DBIusMGS0D0q3mZMA8IHLSSnFyxaiu6mNdk+wHMLGVMO9U7k7VBAD4HyMN/ZP42iFWLYj1USuxNrVzrMwaJWE7e0cGdYE4MRuInLRSnMwYnC4uJyprxShrZ4RqfuVkZw8AQOdpHQNGTso1hfpmSus4M2I74BA5mdIvRURUgLmAOGlltw7b1Ms5B67QRk5QdwKAUSInxhEncREh7RY54eL+6/+7jib1SVGHz/m6W8e+Y0dGTmwT35k/ndbNZ8sCfIeBApj6oEKJnMQoPwrNoS2UhdcJAAapOQkyXuSEI7rePgH6atNh+nVvET357Q4nNSf+iJxUO03rjM5O9tmyAN8BceIBbPxzUpmCGR0e7NbEyVAlegKXWACM4RBrFI8Tx0no7Znauf3D38Tvn18iJw4W9jKtM7pHMr1x5SkU7sP6F+A7IE48gK2SpcN1bLh7ffU2IzZETgAwRFrHQDUn7BYqR2Z4O7UTq3ioMN9sOSJEgT/EibbmpLa+URiOMS/9cSidOSDNZ8sBfAvESSuKYfnEyt2wpqw7QbcOAPpGBjeNlNZpT6+T4KCmJ2e+biXWipOjpVX0v7UHhTUD//7KWhRgTiBOPEC6v/IcB07ZuHtmw6BbBwBjpHWMFDnRusR628K+1iHaW1JZq2kl9t2hIzslRghG9jp55Ottqn2Du7/BwJhAnLRq6JX7H5vMX3M4EgCgX4xYENuekRPH3yw2lazxQ+SExdfQzAS7xzIS7b2lgPmAOPEAuWN6UoCFyAkAxsCI9vV2w/+8PJnYMRV9rKxabef19ZC5Ed0S7e7jZM/8QJx4gCwGC/cgcqJ266DmBACDFMSSoYiLtNoafLf1SLuKk9X7iqm2oZESo0IpIzGSfImjGOqfjlk6ZgfixAOq6z2PnIQokRP4nACgb4ye1lmxu1DtZPEGjtGJX/YUiuvBGQk+r/dIiLLvjrxnSm+f/v/A90CceEBNXetrTuBzAoBRxImxfha17bQFZVajMm/AURIt5Uq34sQ+ncjXxEfaIif3T+tNkUr7NDAvxtoLdRM58SStA58TAIzUrePYQqt3TuuRTH3TYsXtV5bsofdXH3Cr8/DbLUfUVLUz6uqb/mZdPbobXT06i3wNp5KcGc8B84LZOq2KnHiS1oHPCQBGoFFGTgzYohodbv0p/3HHMfpp5zGaOTTdzkTNkXd/zaVnF+XQ8K4J9OVtY52+Rv5m8WC9o2XVdNbAznTbxGy/tPAmRNkiJ7FujA4BxgffcntHTqRDLLp1ANA1Rhz85yhOGA4AHS6por5prsXJD9sKxPVvB0vopx0FNLlfqsu0To9OMfTW1SPJn2hrTmLcdOcGxsZgAUzjRU5CQxA5AcBQrcQGjJzEOMz6OlTcfGEsz8mRvLFsX7MFsbLjUA9Fv4wBtSNoBRAnHlDThpoT9OUDoG/k+YMhIydh9kFwjpw0h3ayL6dsnCFPqORvmD/RnhDqYXlA+4O0TntHTtBKDIAhkB11hoycONRhHDphEx+OsM39iUqbm+yJylqnr5MnVGE+tKpvjlsnZtPOI2U0JjvZ34sCfADESStqTjwRJ3LHRloHAGOkdWQRu5GI0dScMIdOuI6c5GmiJkx5db0wiZSeTBL5mxWmk0jFn6f39fciAB+ij63OaA6xHpxJyB0baR0A9I2MbhrNhM2xINZdcTKoS7zd3BxXnwfSKMAfYKvzgNYMvZLFZI6GRgAAfVGqpDq0xZfGFSeV4mSKJwm7qjfp3jGa4pR0UImy7g989jtd/NqvImoia+wgToA/QFqnFVOJPYqcIK0DgCEoqqj1y1C79ujW4ZqSc/61ggrKaujn+yZQSlyE+tyBIqs46ZoUJfxDyqrrhYjh6O5nGw6J5zYeLLGldXRScwICC2x1rZlK3IqCWKR1ANA3xRXWibvJBhQn0Q7dOsze4xV0sqae3ltldYz9eWcB5RZWqJETFifSeZXFzJFSWyqoqq5B061jvDQXMD6InHgAIicAmJdiNXISTkYviNWy/kAx/Z5XQtfNXS/uZyVHietMJXIihdncX62D/ZjC8hpbtw7SOsAPQJy0c82J3LHRSgyAUcSJ8WtOtFTVNdK+wpPq/f0yrZMcJYTKMiJ64psdomtHcqy8Bmkd4Few1bVz5ESmdWRxGQBAnxg6ctLMvJnq2oYmJ0d80pQWF6Ha1muFCXOcIyfo1gF+xKtb3dNPP00jR46k2NhYSklJoZkzZ1JOTo7da6qrq+n222+n5ORkiomJoYsuuogKCqxzHkwZOUFaBwBDYOyCWNfipLKuXu1EkmQkRoqWaZ5o3KNjNKXHR9Blp3ZVn+f6FGnkBnEC/IFXt7ply5YJ4bF69WpavHgx1dXV0bRp06iiokJ9zT333EMLFiygzz77TLw+Pz+fLrzwQjJS5CSiFZETiBMA9AvXV8jogSELYjXiJCLU/vepqraBSh18TLiNWJ48/XDPeFr24CR6+sJB9Mrlw9XUj/w80uJtnT4AGLLmZNGiRXb3586dKyIoGzZsoPHjx1NpaSm9/fbb9NFHH9EZZ5whXvPuu+9Sv379hKA57bTTmrxnTU2NuEjKysrISN06YdLnBGkdAHSLtHBn/zUj+pxEaX6TOsdHiq4cSWVtA5VU2fxOUuPC6Z6pvdX7WmfYaQNS6d6pvammvoFSYiNoYJd4VcgAYJqCWBYjTFJSkrhmkcLRlClTpqiv6du3L3Xt2pVWrVrlVJxwquixxx4jPcA7rLMzk0BP6/APYZeESBTOAcNSdNJ68E6MCjPk4D/tMqcnRNiJE24LlrN0/npOP7p6TJbLVA0/ftfkXj5YYgCap92OJo2NjTRr1iwaO3YsDRw4UDx29OhRCgsLo4SEBLvXpqamiuecMXv2bCFy5CUvL4/8RbWMnIS0wufEpN06y3Ydp0nPLaU7PvrN34sCgBeKYY2X0nEkyGFwIY8MOqZMHub1Qw0JCOjICdeebN26lVauXNmm9wkPDxcXo0dOapW/NRvvrMwV1z9stxY1F5RV0087jtFFp3TxSMQB4E+KK80jTuqdnAjll1jFSYJiugaA3mkXCX3HHXfQwoULacmSJZSRkaE+npaWRrW1tVRSUmL3eu7W4ef0TEOjRW3Ha03kxKw+J9ougQ/XHKDHF2ynv8zbQrO/3KI+blGmvQKgV7Ycsv4mJccYX5xwCvmSEbbfXeaoEjnhtBUAASdO+CDEwmTevHn0888/U/fu3e2eP+WUUyg0NJR++ukn9TFuNT548CCNHj2a9IyMmngcOTG5fX1kmE2o/d+8rfTNliPi9pe/HRbrvHpfEY166id6YfEuPy4lAK45XFJF/1mR22JLrpHEyd9nDqTPbxlNsYr/CZ9c8e0B6bZJxAAEjDjhVM4HH3wgunHY64TrSPhSVWWd2RAfH0/XX3893XvvvSKqwgWy1157rRAmzoph9YSsN/E0cqLnglgWXLsKytsU2aiosTdv0vLzzmN0w3/XC7fJ/609iAgK0CW7C8rV21FOZtQYjU6xEeI3akRWEsVqxNaUfqkoWgeGwatb6muvvSaKVidOnEidO3dWL5988on6mjlz5tCMGTOE+Rq3F3M658svvyS9IyMnPASLzYs8L4jVnzi5/cPfaNqc5fT9NufFyJ4YVznjlg82iMFj0nHy0AnbYDEA9ILWHfWaMVlkVN677lSa1KcTPX7+AKeRzekD9Z06B0CLV08T3DkzjoiIoFdeeUVcjERrOnW0Ez31mNb5cccxcc0h7ekDO7fqPU40I05kAR7bZO88Wk6/HTwhho0BoCfKqq1ttlP7p1KWgT09xvfuJC5aZCQoKiyYJjg8B4CeQYzPTcFVXed5p47e0zqStggn2YLpio4x4TQkw9o6vr/QaocNgJ4oq7JGToxovtYSkYo526Q+KR6N3QDA30CcNMPa3GIa/NgP9Om6PHVwn6eREyNMJdYW+7pDeXUdvbVin8jVS2dNrYskCxKtrXZmUqS4fbAY4gToN3ISF2E+cdI7LUZcX+zQvQOA3oE4cQHPo7jkjVUiH/3gF5vVyEl4KyMnekzrSDxdtqe+3SlGrE+ds5waLVZhkt3J+iPIaMPHXJAnUzl5yiAxAPREmTJ3Ji7S+MWwjvzl7H60+J7xInICgJGAOHHBk99ut7svIycRnkZOFHHC4kZP3Sr1mjSTp+JkWY61VkUrRqS5U+f4COqjnK0x0eHBNnGCyAnQEY2srHnMRpV5Iydcc9IrNdbfiwGAx0CcuOCD1Qft7rc2cpIQaTU9qm+0qJ0rekC7LFJ4uUuM4p0gmdQ3hRKUfD0PCnNM63RVxAkbQXFECgB/c6CogoY/sZie+z6HypRunTgT1pwAYFQgTjwM/XoaOeFWPlmUdqLCfmy5HooAmZKqOvUs0h1iNWeYXCA8qnsSjevVUZg8nTcknZI14oTTOjyCnm3BOXC0+5jNUwIAf45dKKmso5eX7LGldRxENwDAf0CcuFkg+vqyveJ6QHqcx+8n53UUVdSQ3ooApXukLGx1B62L5pjsjqILYGKfFNr8yDQ6l8WJZj4JR046dOhAfdOsoWVuKQbA30RqzNakODFjtw4ARgXixAkFpU1FxN7jFdQxJozuPKNXq8WJJwKgvZE/yJJCZWS8O2hrVCb2sRW/sghhOsXap3WYPoo4yYE4ATpAK0T2FVaIa6R1ANAPECcuZm0wWcn2hmEPz+hP8a2Y6qlGTjwQAM1RWVsvDM08ScU4wpbyWgpP1rSqXuXsQU3N27TDxaS/S1aytdX4SClcYoH/cVYEDnECgH6AOHGCPICmJ1j9OWTtBNdTtAZvR05u+/A3uvDVX2n+psOtfo+Fm/Pt7rO9vCc+J8xnt4y2K36VaOd3SGddrUDTU9cSCExY4DuCmhMA9APEiROOlFrHi3eOj6TJfa3+AG9eNUJNW7S+5sQ74mRpznFxzcP0PIFrSzjawkJkifIesobGVeTk0a+3iYtWUMjIiScTXOVnwP/PH99cTdNfXK5r7xdgbiocxAmPy4o2wdA/AMwC9kYn5CtpnS4JEfT3mQOERXtGYutnwsix5doBY61Fa4OfEhvhkTCZ8e+VxPLq/KHp4v6wrgk0LDORtuWX0XEn4oRFyNxf94vbl53aVa0bkesh18sZd53Rkz7fcIiuG5tlJ064docv8nM28iwTYFwqahqadKAFeTDQEwDQvkCcNCNOOidEChOjto5RD1F+9NpSI6L1Z5DwMC93OVZeTTuOlKm3mYtPyVANqJyldbSeJBsOnBDihKMd0hclNtx1jv7eaX3onqm91WiTFCda5P8NgK+pcPAcQqcOAPoCaZ1m0zruRyaaIzgoSDViays5R0+26uCuLcblzpzwkCCaMTidOik1I866daTxnBQnzLr9xeKaNQe7vzaHNg2mLZJtzfID4E0qHcwAzWhdD4CRgThpJnKiLYhtC8rsP69ETnYVlNuZp7mLY03JmQPSxNliR6Xtt9BJ5ETr98IeLRy1uf2j38T9C4dlUIhcMTfgIlkuKnbltQKAP2tOzGhdD4CRgThxUmch7ay9HTlp8EKXitZhtbSydZET5g/KlFIZOdHWnHDqhoWU7LSRgu36/64XrppDMuLpyQsGerzs0v9EprkQOQF6SetAnACgLyBOHDiiRE242FNr094WgpXshnfSOtrISW2rIicsutjZVSsYuOiXBQn/aI9/dgld9c5au7TOroKTtOfYSUqNCxedS+wK6ykPTu9L147NorMUbxRHcfLpujz601trIFqAzwtikdYBQF9AnDiQr9SbpMd7J6XDBHupIJbTLPuLbJN9OYrRGnFy37Q+6jLJQlVpYb9s13ExoG/lnkK7yAkTFhxE/7lqBKXGtS6iNH1gGj1y7gA1WuMoQh78YrP4f99esa9V7w+AuyCtA4C+gThxETlJT/BOSsebBbH7jlcIESGFBXfNOIanXSELXh86q6/o0pGEBgdRouJ6y6kdjqBIqjSRE6Z3WgwNzkigtiI7I7TDB7XItBoA7cGxsuomwh7dOgDoC4iTZtqIvYW3CmJlMeywzAR10rG7zq5y4J6jJT8jIyFHSqrphEacSCfY5jpuWkO8EkLXzvfRmryFyjwYAO2A7DzTAut6APQFxInLtI73IidBSkttWwti9x6zthH3So0RtR9MQZl1eVuy6s45avU4GZqZ2OR5OfeGu3GKNRb7jmmX1tSZOEMeCLTdOtooCncBcbHvit3HRaQIAG/Bxd7SWFBb8I6aEwD0BcSJi7k6bF3vLUKUSEBbD7SydZhrNqQ77KVvrm6xJXfr4TLi/zotLoLSnIiubh2t0RSuZ9EOBHQMfXtLnMj3qdHUtEhjOIYLca9+dy1d+fZamr+x9fODANDCkcsHPv+d1uQWU3RYMN14eg/1OdScAKAvIE4cyC9RIideTOuokZM2ihM50yY6PETtsmG+23JEXLMD7AWv/kK/7Cm0+7tNedYw9pDMeKfvq42ccD7eVeQkMtQ7mwsbwDHVio8KW/JPnbNcfZ7t8TfllYjbX/9uP6AQgNbyj0U76atN+aKV/bU/nUJDMm31U0jrAKAvIE40cN2DzYDNe2mdEOlz0lZxohSKxkSE2E04llOAr3l3LW08WEJXvLXG7u9+zyt1mdJhuil1KAeKKu2iJSUOU5R7pVhn63g7crIu1+o6K9l73OaCK9NXALSFd3/JpTeWW7vA/nHRYBrfuxNFaMQ2IicA6AuIEw0nKuvUuTHO0h9tLYh1V5xwhERbIOrY/sjTgLUD87jl98vfDlFBmfPiWBmFaClyknei0k70yMhJZlIkXTW6G101phu1R+Rk/ib71M3vyvIyn64/RHs0xnMAeApHFh9fuF3cfuDMPnSR0q2mTVOiWwcAfQFx4qSKn2szwkO8U1/haUHstvxSGvLYD/T3hTtcR07CQ+jP0/vaPX7vp787fT+u5ThcUiVm4bhqA+b15ehLXYPFbsaOrHGZPiCNHj9/oNc+E3lQ4EjNJa+vos82HLJ73lHDXTd3vVf+XxB4rM0tprs/2US86/3ptK5028Rs9Tnt7oiCWAD0BcSJhk/W5Ynr84ame/V9ZUGsO63EK3cXigjLRqVOREu5puaEz/SuPK2b09oQNktzTOn0SokRosYZPCq+W1LTFmNpj++tQljHyAmzdn+xOEhcOKwLfXHraNsyabqJDxbbjOcAcJfdBeV0w3/XiQ6dqf1T6bHzBtoNo8xIjFSne8vWfACAPoA40bD1cKnqZOpNZOTEHRM2toh39ACRSMM1KTK49oTZmm9dbkm8YqrGbFHWaUgL5mndlNSOFhk58bY4cfZ+fzmnnyhCZlHC6/fONSO9+n+CwOMfi3KEod/wrgn0rz8OU80Ltdvhlken0Ya/TrUTLQAA/4NYpsYLhG3bmR6aeg5vIH8U3ak52S3FiROXVG1aR3u9fr99lEU6vjIFim9LVyeRES3OzNlkREYb6fAGju/3+p+GU0fF0v6DG0aJNmltISwfN7gGBwcQ4Al5SsTtnqm9KTLMucD21vwsAIB3QeREYX9hpXpgT/CSE6qn4oQPwHtdRE7EUL7aBruIiRQnssXY2Q+unDbcUdN67IxuTgSZXN7sTjHkTcI1kZM+qbE0faB1ECDDAwl7psSIdXj/+lPFY5z2gaU98BRpKCjnRwEAjAPEiUJuYYW41nbBeItgNwtiudtG1pVw15CcClzf0GjX0eIYOZGkKAKkUfP/yIF/ctieJ5EThnPxo7OTqb0iJ46hdi2n9+pEcYoQ0/qvANASLPTlKAaIEwCMB8SJwv4iqzjp3g7ixN2C2N0OLbNsRibbaWU3DhtIyYO7jKAwo7on0T8uHqyamknk7B2taVtz7cSOjO3ZsV0LYuVn4wo598dVmzQAzmCRL2u8vDUTCgDgO1BzonD9uO40rX9qs2fy7V0QK4thJWxLz6Li553H1McSokLV2otYTeSEJw3LLp26eot69ljoZlqH54xwlMIxfTKlXwp5G23tSEufd3JMGO0+RlRUAXEC3EdGTbgTx9viGgDQ/kCcKPAPWK9U7zigOiIPwC1HThzEiVJ3IlsemR6a+o+kmDA19XLWoM60Pb9MjZwcZA+RN1YJ7xKmo/JaV/CwvY9uPI0qaxvom8359N9VB8TjZ/T1vjhxlvJyRbKSjirS+K8A0BLFijhB1AQAYwJx4gPUgliLp5GTevXsT6KNlnAx6UNn9VU9TEKVFEltQyM9/NVWtfsoNiLELQO1gV2sDrI/7igQ14Mz4ilFSau0Fy1FTjoq9QLyYAOAO0inY9SbAGBMIE58gLvdOlKcsJjgehM524ajGZJLRmbapUdumWBzvAyVaZ2GRrvpwp7ODRmQHieuLxputfluT1qqOUmKViInSOsADzhRYY06JkKcAGBIIE58ABextiROik7WiOgAZzlO79WRvt1yVG1vZg8Whh8/c4Brgzg5AJBTObXK3Bopdjzh/KFdaELvTj6ZNxKsDEVsruaEQVoHtCpyovH8AQAYB3Tr+AB3CmL3Ka3M6fGRNFQZ5b5893FR1CojJy3Vf2gjJ3KAIePKtr452OvFF6ZnLQROKFk58y1CWge0puYEkRMADIlXxcny5cvp3HPPpfT0dHFgmz9/vt3zJ0+epDvuuIMyMjIoMjKS+vfvT6+//jqZHXcKYverPitR1DctTh1E+NaKXFWcRIc1LzJkzUkTceJh5MSXtBw5kQWxSOsA9yivrqP3V1sLupNQEAuAIfGqOKmoqKAhQ4bQK6+84vT5e++9lxYtWkQffPAB7dixg2bNmiXEytdff02BXhArh9vxjBtZ88Gs21+sztRxZcEtUVuJRVrHJk54UKDeuGJUV3F99+Re7qV1EDnxG7sKyumhLzbToRPGGMB424e/qR5BiJwAYEy8etQ666yzxMUVv/76K1199dU0ceJEcf+mm26iN954g9auXUvnnXceBXJB7IEiRZwkRYlowbVjs+jdX/YLa/oqxSk2OjzYrbQO/z/yb8T/r8OZNE/MHEh/Obtfi8JJpnX4YFNT3+BW1xHwLm8u30efbzhEXRIi6c4WxKS/YBflP729RghzjjhK0EoMgDHxac3JmDFjRJTk8OHDopZiyZIltGvXLpo2bZrLv6mpqaGysjK7i9FQ7etdiBP+LHYcsa5XN8VGflKfFDV3rkZOQltI62icV7WREz3CaT93IjrcaSQLimUHBvAtsovMcYaTnthz/CSt3ldsJ0yYxGgUxAJgRHwqTv7973+LOhOuOQkLC6Pp06eLFND48eNd/s3TTz9N8fHx6iUz09ZKa7iaE4tViDjy445jwoCNzdRGZiU1SWdU1bobOdFfhKStBAV1UEPz0u0W+A7eXvcdt4oTOetJj+wusC6jY4cZIicAGBOfi5PVq1eL6MmGDRvo+eefp9tvv51+/PFHl38ze/ZsKi0tVS95eXlkNLRGY47REy6Sfe77HHGbUzmyADRZ8fdgG245DDCqpYLYFopLjQo6dvwHf+bSDLC6Tr/RuN0F1rlUZw1Mox2PT1cfhwkbAMbEZ5WSVVVV9Je//IXmzZtH55xzjnhs8ODBtGnTJnruuedoypQpTv8uPDxcXIyMnTixWOw+9AWb8ymnoFx4kdw8PrtJOJrbj8udOMW6ijJwCsSxZVmHJSce0VEItnIqhhGbz9l33NpFxlRrvHP8zdbDpXTTe+tpVI9kundqb5HWYXqmxIjC8X9fNkzMppKDIwEAxsJn4qSurk5cghzO7oODg6mxUb9nZO0ZOeGW3xcW7xK32ek1XmMYxYWfbFUvoybcieNOiJoFisgfadB2/xgRefYLIzbfI1M6ekvrrNhdSPml1TRv42FauDlfnSGVnmCdQ3XukHQ/LyEAQDfihH1M9uzZo97Pzc0VkZGkpCTq2rUrTZgwgR544AHhcdKtWzdatmwZvffee/TCCy9QIJiwOYqTT9fniS4dHsrHKR1HuneKps2HSmlIZgI9fE6/FluJHQthv7xtDK3YVUjXjOlORgbtxO3DP7/fKQqObxrfw6XhnjQH1FtaRzrAMlKYML5wNQYAGEycrF+/niZNmmTna8Jw+/DcuXPp448/FjUkV1xxBRUXFwuB8uSTT9Itt9xCZkZ2mzAySMRnof/6abe4fceknk7rSd648hQxXfjU7kkeu7WmxIbT8K6J4mKamhMUxHoN9ix5Zclecbukqo7+PL2voSIn0gGW3Y+1XUQQJwCYA6+KE/YvcdaNIklLS6N3332XAg1tWqdeUSfvrzpABWU1wjviMsWQzJHO8ZHi0hoGZ1gt8M2AzSUWkRNvUVJpa8t+beleWpdbTC9fPpzS4iOaqTnRUeREESfsqLz1sM1eAOIEAHNgzvYOncFRD6lPuCCW7bVfXWpNf909pVe7GItx14JZQLeO95FF1jKqt/7ACVG7oYVroqRzMVOtmY7tb4qVtA47KmuJgzgBwBToz9fcxNGTxgaL8Cx5eP5WOlFZRz06RdOFw7p49f/5342n0bb8UrpwuHffVx81J0jreAsWyMyALvHE8mRTXoldNIXJK6606/zSS7fOOytzaePBEnG7u0accOaTi8gBAMYHkRMfF8V+tSmfvt9WIG4/eGYfClEs573F6OxkuuF01wWORkR6vhQjreP1yElcRAid3qujuM2tt85SOtLcr7makyOlVTTm6Z/U7rP24nBJFT2+cLt6v3tHmzjh4l7RrQYAMDwQJz5Chs/5bJQZkhFP0wd29vNSGYMkJXJSUduguuUC70RO2F+HD+rWx+zt6XOVTp2eKbEtduu8vSJXtPbKIu/2Ynu+/fiKLI04SdC04gMAjA3EiY+QZ3THyq2pibE9rWeroGU4VC8nLiO14x1kh0tseKgQKExZlX3kZH+RVZz0S5PixLUw1KZ85Cyo9qCy1vbep3RLtIuctOSgDAAwDhAnPu7YKSirtutAAS3DKSpZdyJbSEHbkFESETmJdB45kZOyeyvipKa+0WU3XllVfZNBge2BjJxN7ptCX9w6xq47J92h0wgAYFwgTnyc1jmuRE7YeA24D1xivYuclxMTEWKLnDjUnBwotkZO+ijiRAoUZxzQdPW0qzhRojfSkJBFP1vWM/dN69Nu/y8AwLcgDurjgljZDiuLPIGHXieInHi55iS0Sc0Jp3zu//R3yiuuEvf7pNrECad2IkKbtr4fVFJAju6t3qZSiZzwBG/JRzeOosqaBrv6EwCAsYE48YNLLCPTFMA94BLbNp7+doeoyWBfHce0jmPNyRvL9tKibUfVyETn+Ah1oCRHLhzt/Tjiwq3xtvvtV3Mi6160QzBTYiOIbPoJAGACkNbxEakO+XCIE30Ysb2/+gCNeGIxbThQTGa2qn9j+T6a8+MuMXtpxe7jtGzXcfVzlTUnJ2vrqbHRotaaMAmRoXY1P4XlTT9/HrGg5WQ7ihM1coLiVwBMDcSJj/jPVSPokXP709T+qXTrxGzr2R7wu4U9G+IVnqyli15bRWZF237NnTRXvr1WvZ+ZFKVGTrjWlSMq7Azr2J6bFheh+pk4onWR1aaMfJXWAQCYD5x++IiOMeF07dju4gJaHzkpbMe0DrepmrEdlf1hJDzkTwvPduLxCZwm4QN/SVWtnTiJVhxXeebO74dK1W4zLdpIC6MdxOeLtA4AwHwgcgIMQUZipJ2JnTdwNHTT1k2YCW0kw1HcSfGRGGVr1dZ25MhKKVvkpKk4Oah09XRNilL+P/u24rdX5lKNl6zvpc9JBMQJAKbGfKeJwJTITgxOIdQ3NHrF9t8xHVFSWSsiCWZDKxZkK7sjidGhwhqe5+twmksiXU3SlOnYR5uJnAxIjxOfabkmcjLlhWXimr+zmydkey2tE4W0DgCmBpETYAj4zD0iNEh0jBw60bTuoTU4+nGUBkDkRCtOHjjT5gsiIyccWckvafr5pidYIyf7FUt7V+LE8f+TbDhwgrwB0joABAYQJ8Aw9v9ZygRaOfOlrazbXxwgaZ36JmkdNrW7fVJP9fEERZws2nqUSh3qUphhmYniesvhUjsLee7+kUWyPOFYDgxkEaF1k/WWmJCRE6R1ADA3ECfAMKQodQ/eMvlak2svTrgY1IxoZ+ZIceLY7ZKkdOX8tPOY3ePdFEGYmRQp7OHrGiz024ES8RingX7dW0iNFuv79dCYoD3z3U679BAX3XqzTghpHQDMDcQJMAzhIUHNWqh7AkcHdh61Trid0LuTuOZ6i6Ol1V6LzOgFrSnaccWnhFNkWmTkRHL35F509qA0enhGP3GfvU6kjT2nfRoaLTT2mZ/pmnfXqcWw2jk3c3/dL8SL9vP+cM0B+j3PKmw8heuBXvpxt/qeZuyqAgDYwB4OjCdOmpmO6y5susZZB55q2zs1RpiSHSiqoLP/tUKcna/48yTR/m10OLWiTdMcl5ETh7SInF3EcISExYmcpC1hq3ubI6x9lKlrcpQQOLdMyKbXl+0Vjy3LsRq9MT/uKBCus/07x9G3d5/u8Xrc/9lm8R5y+bp3glU9AGYGkRNgGGRqwBuRE5nSGZmVqEYNPl1/SLTSskX7OoeUj1G58b31NG/jYfV+YbnztM7pvTqqt6cNSGsiTOSQQOlj4jgdupvSRvzQWX1pTHayuP3OL7nq81zI7Krbxx225ZeK67+e049+vn8ixSgt0AAAcwJxAgxDuJKK8IY4keLj1O7JlO3kLNxb3SX+5scd9jUkMnLiOLyvR6cYunlCD9FKff0450aB0kmWC2wdnXq7JVvFCTO2p1XoOCus5fQMW+R7AhfgSn+Vi4ZnOB08CAAwFxAnwIA1J21L63DaZvMh65n4qO5JdHova82Jlq3KmbqR4boQR7i7hnF2gJ99Vj/65aEzhKW9M2KVaAXPzimqsPdLydD8zThFnDiDF4nTQp6wv7BStdJP1KSfAADmBeIEGC+tU9e2yEneiUqRZoiLCBHOs+ySevP4HqL+5JkLB4nXaDtNjIq25deR1symkTUnztI60kGWGdglXny2TK+UGLrzjJ70l7P7UrRS5+Jpy/augnJxzd8PACAwgDgBAdetw8Pv5MGWu1CY2Wf3oyX3T6ShXRPEfceDrxGRniCSKf1S2yROZJ0HRz4c0zpacRIc1IHGZFujJ6N6JNF90/rQTeOz1aiHJ63gnALiicrMaT2stSwAAPMDcQIMQ5gX0zqujMGSNAdQZ2kRX3XYaP9vFkpbD3ueZnIcwMetwRLHbh13kDUnziIncnqx5L5pvWnm0HS6dWLPJi60JzwQfgs259OOI2UipXTT6T08XmYAgDGBOAEBFznhbhxX4kQeQLnNmIs3fQ07q058bild+fYa1WH1nk820Yx/r6QlOfbFrS1RWWMTcZP6dKLJ/VIpNLiDXXGxJ8RoCmIPnbCfSyQjUJJeqbH04h+H2c0qkgLG3bQO18c8/8MucfuWibbICwDA/ECcAMMQHtr6mhM5k0Wb7nAWPQgNDlLNxPyR2uGDPs+q+XVvEf12sEQIFPZgYeYsth6o3aVCqTnhiNMbV44Q6yU7aWRxqyfEyZqT6nrKOWqtA/EENSrl5uf6v7UHxSDBTrHhdO3YLI//PwCAcYE4AYbv1tl8qKTJmbyW577PoQGPfE9PLNwuDva2tI7zA3SychAt8oM4qdBEO7747RAd0wzqY0HgSUpLFsT2TYtVU2J/Pac//eGUDPrDiMxW15xwO3K+0trLXDisi1t/nxJrNbUrcMPrhOuC/v3zbnGbDeHgCAtAYIE9HhhOnCzJOU55xZWi5ZXtzM97+Rfx+P5nzmnyN+z6+vKSPeL2WytzKSo8RJ0j46rugs/w9xVWNCn69HUR69eb8umjNQfV+5zOWrGrkKb0txW2NsfJmqbpq54pMfTPPwxp1bJ1SYykfp3jRA2IuJ8QSR/fdBqla1I3zZEWH+m2EdtbK3JFxxR36Fw60nMhBQAwNoicAMOgHR532X9Wi+t9x0+6LAD9y7wtNOGfS+0e+9dPu+nbLUebHR4nayM89ePwdvuv4/rIAlG330v5e2+5qXLKa+Gd4+jda0fSH0dm0mPnDRACkbtz3KFzvLWjZ+HmI7RbaQ92RtHJGnpz+V61sJb/XwBAYIG9HhgGbRHnoRPWAXBKzagaJZFwMas26sB+JhcOt6Yf1u4vdlkQq/XzKPeDOKlwaP+V/G1Gf3H94/YCu/oZd97LmykRFiKT+qTQMxcNdjuCI0nVtBvf8N56l6/jSBcv+6Au8XT2wM5tWl4AgDGBOAGGS+tI+CCtjW5wIalko8P02+iwEBqQHm/3WKSLg7baMquZ5usrZLSjh8ZwjOfecEEop1H4oL1k5zGP3is6XB927zJy4vhdaeF03QerD4jbf57e1+mMHwCA+YE4AYZM6zDs/aGtC8kttEVONh50ECfhwdTVwZbdVeTEZjZmPbjP23hItPNyQe2eY7Y0Unsgox3ssqqtE+FW3RmDO6tpEXc4qaSI9FJMKgtiGekW68gLi3dRXYNFWOCP0wwjBAAEFhAnwDA4Rk4ufn0VPfL1Nru0jqzT2HjQfnAfW9RnJtkXbraU1uH3qm9opHs++V1M9uWC2ikvLFMdZl3B3UDc/uvpgDtttIOX7Zu7xtFlp3alWZN7i8fOHZIurn/aWdDiMljfq0Fddz0QEhxETyvjAeRnrIULbedvOqxGTQAAgQvECTAMsh3WFZ+uP0SDH/2e1uwrok0OaR0+2Gcm2kdOXHXr2MzG6uxaeyUfr8tz+necZuJW339+n0NXv7PW5euao1I1iLOmofhgHq8U6A5Ij6Os5CiqrmukH3cUuO1z4ipK4Q/GZCe7rOd5dtFOUUN0zuDONCjDPgUHAAgsIE6AYahraNl8jYMVV7y1RriYaulAHUQEQetY6ipyIofWceSkvKbpQfTtFfvU6b7aaMmEfy6hma/8Sst3W03TVu8rUp9ftPUInffySrvuIk/rRKypnXS3UzsycsLt03pBGrlx+oqjUhIWlNwiHhLUge6f1sePSwgA0AMQJ8Aw9E2Lo7E9k8WlOXjiMHNqVpL6WKPS1tMnLVZ9LNpFLYasOWGB4yxywgZkMv0gWbmnkArKakRqQtalbMu3zcO55YPfaPOhUvr7wu1t6rCZMcRad7Is53iLrc56jJzIqBQjBSQb4z2zaKe4zZ4mmD4MAIA4AYaB21g/vOE0+uD6UW69flg364RhRpZ/aD0/RmjEi9Oak+p6OukQOZk1pZe4fuzrbbRfU4D7y57CJu/DRm5a3xLxni3UisjXu+qw6ZMaKwpkaxsa6Ydtzad2ZF2KXmpOGPYskRErKa5+2F4gCph5UjK7wQIAAMQJMByOQ+ZcMSwzUb0th+hdObqbuL5ubHd11ktz3TrSZVVy0/gelBYXISIci7dbxQHXt8z9dX+T9+H/cseRcrt0lJzb4wyeRLx8l1Xk8IHa1bqfq6Z28oVt/9kvraDPNxxy6TYbrZNuHcfUTlmVteCYa3SY68ZlUYrGCwUAELh4VZwsX76czj33XEpPTxc/ovPnz2/ymh07dtB5551H8fHxFB0dTSNHjqSDB21mWQC4wyuXD7e772yQXf/Ocept2TczMiuJNj48lf56Tj+X7y19TvjM3rFwk9Mt0wemidsnlKnFbIzmiLTn2J5fSvklVsO4lop6f9h2VI2s8LA7V8jUzsrdhXTNu+to+5Eyuv+z312mdaJ04nMiiYu0fr6lVXX05W+HRRqMXXlvnpDt70UDAJhRnFRUVNCQIUPolVdecfr83r17ady4cdS3b19aunQpbd68mR5++GGKiMDZEvAM7ug4T2mtZYZ3s0VJtK6wEm06JzE6rFlzL3YyjQgNEkWvmxz8Uuym6yriZG2u1XFWy+R+VvfUbfll9LPGNI2jBa7gNBDDHTnje3Vy+brsTjFixg3X1jTnuyLrZbxlX+8tOiszdt5csY/m/GidtHz7xJ5qRAUAALz6q3XWWWeJiyv+7//+j84++2x69tln1ceys3G2BFqHVl/wAX2Z4/NBHejZiwaLlMtDZ7nvm8HRjRHdkkSR609O3FgTldbe4opa0T4s25ZP65FEq/cVi8jLzKFdRNrn2y1H6KtNtnk4UtA4Q07r5Y6clpxR2ZBNDuAT69rBdc2Jq64kf/HAmX1oTW4RLd9l7WpKj49Q020AAODTmpPGxkb65ptvqHfv3nTmmWdSSkoKjRo1ymnqR0tNTQ2VlZXZXQBg7prcS0QQ5lw6hO6c3EvMYpHpHR5Mx1wyMpO+vft0tyfnSsYoHUFa19lrxmSpkRfmREUd/Z5XIopTOQ3DKSNmeNdEGtglTq1bqaprEJEYpqTSdYfNkVKrOEnT2Ly7QtadSHql2LqQGK7l4CnGTLTOak7Y/fb5PwxV78+a2psiXNTYAAACE5+Jk2PHjtHJkyfpmWeeoenTp9MPP/xAF1xwAV144YW0bJnjOa+Np59+WtSnyEtmJsanAys9OsXQd3efThcMy6COMeG04M5x9Psj0+jtq0fQ7LNd15S4w59O6ybO6CWXjMigR861Dt9LigpTBwj+/Rtra/Cp3ZPozAFp1Dctlq48rVsTw7f/XDVCrbNwxVEpTtwoCu2abP/+FrWqxt7MTY81JzIt98IlQ0T300XDM/y9OACAQI6cMOeffz7dc889NHToUHrooYdoxowZ9Prrr7v8u9mzZ1Npaal6ycvz3HUTBA6cDuF6j+a6YtyB6x+evXiIer9LQpTaJSQjJ8zWw9ZI3qjuSSIisGjWeDGtl5djUp9OqhX7QGXoIBe8fuXgkdKayAkz99qR6m1HUziZ0gkN7tBkJpFeuHB4Bs2a0lu0iAMAgF/ESceOHSkkJIT697eefUr69evXbLdOeHg4xcXF2V0A8AU8eO7m8T2INcnQrjbPFGctyKO6NzWG+8fFg+m9606lWyb0EN0o0p327o830fr9xU3SMEUVNWpBrjtM7JNCX98xVtzmYXnOimH1MvQPAAB0KU7CwsJE23BOjtXTQLJr1y7q1g3FcECfcHpo8yPTaEJvW/dMcnSYiEhIWHj0Solp8rcpsRE0vncnEXHhy3XjuqvPsVuslpKqOuGLwkJIFty6a2rGyPqSJmZuOiuGBQAAn4sTrinZtGmTuDC5ubnitoyMPPDAA/TJJ5/Qf/7zH9qzZw+9/PLLtGDBArrtttu8uRgAeBXHCbo8XXfLo2fatfa21F3DXDsmi3qnWkXMXocZO9z5w3A6it/fXaRviuPcITVyorM2YgAA8Lk4Wb9+PQ0bNkxcmHvvvVfc/tvf/ibucwEs15dwK/GgQYPorbfeoi+++EJ4nwBgJLTdJVo/leZgAXPrRGvrvKM/SdFJqzhx5VrrijBFyLiqOdGTdT0AALiLV3+5Jk6cqNqEu+K6664TFwCMDrcrf7P5CN031f0puj07xaqRE7arv3buOgoL7kBnD+ps1wnU5sgJ0joAAAOD0yoAWsnTFw6ix88f2KwlvSM9Olkn7haerBXmbdKIjAfftSZyImtO2C22sdGippfkXB0UxAIAjAgG/wHQSrjI1RNhItMs0j9lWY7NfbaoopVpHc3/z2ZwTdM6iJwAAIwHxAkAPiZb6exZkmONmmi7czyPnHRwIU6UicSoOQEAGBCIEwB8DHf3MFsOW9uJ2eH2pvE9hBnZyO5WC3xPC2Idi2LRSgwAMDI4rQLAx/R08ETJTIqka8d2p3tbMWOGU0scPWETNm1RrCyIRc0JAMCIIHICgJ8iJ5LpA9PEdWuH3zlrJ65U0joxSOsAAAwIxAkAfoycPHnBQOoc79nEZEdCnbQT8wwfvQ79AwCAloA4AcDHdIwJU4tgx/ey2eK3ljAnFvaylTgaaR0AgAHBLxcAPobrRL6563RRtJqZFNXm95NeJ9rhf7aaE0ROAADGA+IEAD+Qrkwo9gbhSloHNScAALOAtA4ABscWOXFWcwJxAgAwHhAnABgc6RL7695C9TH4nAAAjAzECQAGR5q5vbJkL5VW1YnbFXK2DiInAAADAnECgMEZk52s3v5602GR3pH1JzHo1gEAGBCIEwAMzqPnDRAus8z/1ubRd1uPqs9FIq0DADAgECcAGJzeqbG04I5xovZk+5Eyuut/G9XnPJ2aDAAAegC/XACYgISoMJo+wGqDDwAARgfiBACT8IcRGf5eBAAA8AoQJwCYhOFdE/29CAAA4BUgTgAwCdFoGwYAmASIEwBMxEXDkdoBABgfiBMATMSTFwykTrHh/l4MAABoExAnAJiIiNBgWnjnOJraP5U+vGGUvxcHAABaBZLUAJiM1LgI+s9VI/y9GAAA0GoQOQEAAACAroA4AQAAAICugDgBAAAAgK6AOAEAAACAroA4AQAAAICugDgBAAAAgK6AOAEAAACAroA4AQAAAICugDgBAAAAgK6AOAEAAACAroA4AQAAAICugDgBAAAAgK4w3OA/i8UirsvKyvy9KAAAAABwE3nclsdxU4mT8vJycZ2ZmenvRQEAAABAK47j8fHxzb6mg8UdCaMjGhsbKT8/n2JjY6lDhw5eVXQsePLy8iguLo7MBtbP+GAdjQ/Wz/hgHVsPyw0WJunp6RQUFGSuyAmvUEZGRru9P38RZt3gGKyf8cE6Gh+sn/HBOraOliImEhTEAgAAAEBXQJwAAAAAQFdAnCiEh4fTI488Iq7NCNbP+GAdjQ/Wz/hgHX2D4QpiAQAAAGBuEDkBAAAAgK6AOAEAAACAroA4AQAAAICugDgBAAAAgK6AOAGGgd0KGxoa/L0YAAQ02A+BL4A4MTiHDh2iF198kfbt2yfum7H5Kjc3l84991y67LLLqLS01JTrCIwN9kNgpBEwjN4FpunFSU1NDZmVoqIimjFjBv35z3+mH3/8UWxs3pw35G/4x++WW26hXr160d69e2n9+vXicTOto+TIkSO0efNmKiwsJLNi1n0R+6F5MPt+eO+999Kf/vQncTs4OJj0jKnFyT333ENnnHEGFRQUkBmJioqihIQE6tevH3322We0ZcsWMgv//Oc/xbpt2rSJ1q5dSx9//DFlZWXRL7/8QmZj1qxZ1KdPH7riiito4MCB9MUXX6jTt82CmfdF7IfmwMz74caNG2nq1Kn0wQcf0CeffELff/+97qMnphQnrO5nzpxJixYtolWrVtHcuXPJjOzcuZNiYmJo3rx54vbXX39NJSUl4jmjh1z5x2/OnDm0evVqGj58uFhPnkYtQ5Ly2ui88847tGTJElqwYIH44T///PPp4YcfppdeeonMQCDsi9gPjY/Z98N169ZRly5dxP53+eWX0/33369GT3S7jVpMyNKlSy233nqrZeXKlZbnnnvOEhcXZ9m9e7fFqNTV1dndb2xsFNf79u2zTJw4Udx+4IEHLEOGDLFs27bNUl5ebjHLOjL19fXievjw4Za7777bYiZmzpxpOf/88+0e4+9y8ODBlp9++slidMy0L2I/xH5oVI4ePWrZvHmzuL1kyRJL586dLS+88ILd96o3TBE5qa+vt7s/dOhQuu+++2js2LEix5aenk5PPPEEGZG//e1vdMkll9Cdd95JO3bssMtnr1mzRj1zefbZZ6m2tpauuuoqMeKaz1SNvo4y5MjqvrKykjIyMujEiROGr12QZypVVVUUFBRE2dnZds9zTrhr1670r3/9i4yGWfdF7IfYD43C008/LdKob7zxhtgWmdTUVBo0aJC6T1599dX0j3/8Q6St+HvVYwTM8OLEcYfiH8f4+Hh1Q+Odi38wONe2fPlyMgrHjx+ncePG0fz582nIkCH0ww8/iCp57Y7CPxpjxowRt/l1hw8fpq1bt4qDwfTp08no68g/GPJHhPP6aWlptGvXLjGMSrehyGbCxosXL1a3SV7+yMhISk5OpqVLl9oV4A0ePFjkh7k4j0PNRsGM+yL2Q+yHRtkPc3JyaMCAAfS///1PLPPs2bPpzDPPFOKZkd8V1xBdeuml1KlTJzW9o0ssBuXYsWOWsWPHWgYNGmR59NFHLb179xbhVBmq0oYjmbPOOssybtw4S1VVlcUIfP3115Z+/fpZDh48KO5XV1dbZs2aZenevbtlxYoV4rGHH35YrP/pp59uSUxMtMyZM8cyfvx4y6WXXmrZtWuXxcjr+Msvv6ghR/ldfvDBB5a0tDTLoUOHLEaB0xkcBu/QoYPluuuus+Tn59uFz3Nzcy3BwcGW9957z+7v+PvLzMy0zJs3z6J3zLwvYj/EfmiU/fD555+3jB49Wl2nI0eOiP3wkksusezZs0c8Jp/j7/jll1+2xMbGihSkTMEWFxdb9IJhxYk7O5Q2f7p161ZLaGio2Phqa2stCxYsEBusXnnrrbfETlFTU6M+tnPnTsu5555rOe2009QfifT0dMsNN9xg2bt3r5pPDAoKsrz55puWhoYGi55pbh15J5PIH8WPP/7Y0qNHD8tvv/1mMQInTpyw3HnnnZabbrrJ8tRTT4ll/+ijj9Tn5fdzyy23iO02JyfH7u+TkpIsr776qkXvmHlfxH6I/dAI+2FdXZ0QXVw3oz0Z+PTTT8V2Onv2bKe1UnyiMHToUHFyERUVJb53vWBYceLuDqXlnnvusXTq1EmoyYiICMsPP/xg0Su8M4wYMcKydu1au8e//PJLS0ZGhjgg8LrzjuR4Zvr++++LA4TeaW4du3btavnkk0/sDmyFhYXizOfnn3+2GAH+friYTv6IT5s2TWyf8gdAfm/8XfF3+sc//lE9G//mm2/E2fj27dstesfM+yL2Q+yHRtkPr7jiCrFuHOXSFrnefvvtljPOOENdfynGOCo0atQo8V1ef/31lrKyMoueMKw4cXeHkl8Eh7UuvPBC8UWwgtbbFyGRO8qBAweEYn/xxRfF2aWEH+cd6+abb27yY6j3MzRP1vG8884T35N2Hffv3y+6BPgM3XHdjcCvv/4qzrB5feWBXK43h1T5LIbPXs4880xLWFiYOIAb4Ts18r7oajsy037YlnXEfqj//bBeESIyWrdx40Y7Mcnr1LNnTxFFkaxbt06kXzlqItM6esNw4qQ1OxTnF6dOnWrp06ePCCn7G84FHj582FJZWSnua1WuNvzNirdbt27qxibhH3ZW94xefxy8uY56/mFwF7kOHPrns5VVq1Y1eU1RUZE4E+dtesuWLRYzHtz0tC+yKNKum/a2WfZDb66jnvdDdz9/o+6HlcrvqDPk98g1XBMmTLBMmTKlyWeSnZ1tefzxx9X7HP3SaypV1+LEmwc2Ga7Tg7cC/3DzD3VWVpYozuINSYZ9tT/qvJFxCI7Xm8OMHHLjsxXt+nF+VI8Ewjp6up1q7/PBmfPaDz30kKW0tFQ8podt0xcHN73si7wdcsSDi3J5+f773/86XScjb6OBsI6ebqNG3A9ra2vF5z99+nTLlVdeKcSUXD9tGpW/P/Yy4SgJ13O99tprqhDjIlf2a+ECWD0LaV2LEzMf2LiynQuTJk2aJMKK/GPBhVlcqKXlpZdeEhXU999/v7j/+eefW0499VTLwIEDRW6fw6kdO3a0/Pjjjxa9EQjr6Ml2yre1ZydSvDzzzDOWAQMGiOp6PsvhLo+TJ09a9IRZD25csMp1Lvyd8dnxtddeK4p5+fs0yzYaCOvoyTZq1P3wyJEjlmHDhlnGjBljeeWVV8R3yhdebsfvkdNPc+fOFfefeOIJS0pKiogOLV++XKSlWIjt2LHDYiR0I07MfmD73//+JzYs3uAkV111leWvf/2rev++++4T4XGu/teGUH///XdR7MQ5UC4wdBaK1AOBsI6ebqdcJS8jK/KMhXP1ISEhouaCnSmPHz9u0RNmPrjx2SO7uVZUVKjfCZ9l8nfxxRdfiG2Sz6a5Jdio22ggrKOn26gR98PPP/9ciCfZsl1SUiJa9bmAXKZEuV2d62f4d0gbEfnXv/4lxBYX8/LntGbNGovR0I04MfuBjX8cuMhKwmFFLkZiLwhWt9IvQlsc6Bh+k+FHvRII6+jpduq4fp999pn4MRw5cqRuWzHNfHDjFmc+02bkd8MFvbxufJbK9Qa8jWq3Q6Nto4Gwjp5uo0baD+X+9NprrwnhoYV/dyZPnix8dJjVq1fbfVfafZFvc7uwUdGNODHTgU2qVO2GsmnTJrGh8ZnlRRddJBQ771y8obGyZ0WsTQnonUBYR29tp1q4Sv6NN96w6BmzHNycbaNsmMYhfG4RlbCY4mLB8PBwNTSu13kjgbiO3tpG9bwfslhavHixag7HsEcOp43l74qEI5FcV/L999/rvlDZcOLErAc2dhHkdeCzZnYcdMx98mOLFi2y9O/f386JkA2B+ICXl5dn0TuBsI5m307NfnBzto3K4kH2q7jgggss8fHxIiQeExMjvksubObC3RkzZliMQCCso5m3UQn/RnJ9CH8/7PvDZmiczmE4osO/o1xjoi1+5cJX7oLjAlkz41NxYuYDG4cOOUTIOz8rei7UcgavC+cBtTsbrzcrYe2OpkcCYR3Nvp2a/eDW3DaqrTV45513RIfR/Pnz1ee57uCOO+6w6J1AWEczb6Py94RblblOhuuzeL3YTZlTxOyzUqnUx3ANDa8Xe5ho4ROja665xmJmfDb478MPP6SnnnqKxo8fT/3796dnnnlGPB4SEqK+Jisri4qLi8WUxCuvvFKdlDh69Giqq6ujzZs3k96QEzt79uxJkydPFpMezzvvPDFEii/a1zAsCHmQVkFBgTpQ69tvv6Xhw4fTqaeeSnokENbR7NupO+sYFhYmvrt+/frRSy+9RHPmzKGOHTuKQX08PIwnCldXV4v1N/I2mpmZSddeey29/PLLdP7554vHjh49Snl5eU0m0+qJQFhHM2+jWioqKsTARZ4OzN8TrxcPj+R1LSsrU6cJP/bYY+I35c033xQDJSU8STkxMZFMTXurHxlW48IdLlBiY6Znn31WmDBJNagNvX344Yei4JBDVxJuo2LDHD1VU7P1r2OeXZ5dcyU1h93OPvts9TkZQeC8IleYc0fD66+/LqrM+cyAh4XpjUBYR7Nvp56uo6MvhGMx3imnnKKr79HTbdTxtdz2zN0QnBLgWgX+TPRGIKyjmbdRV98jewLJ9ZW/nfy7wjVsNZo0DtejcOcN+whx2zOnczgVJC32zUq7iROzHtjYipv9LXhn4XDb22+/rT6nXV8OqXLYn68ddygO37H1NXc08KAmPQ1bCpR1NPt2avaDW2u3UW3dAofOucuKvzf+8ZeTW/VCIKyjmbdRV98jp3G0aL+vyy+/XE3X1GgECq8bp3g4Lcefg15/T3UtTsx8YOPhZLxufIbMNQf33nuvqKPgqmqZI5TrwRsTG1Jxbri8vFw8ph0Cxhsk963rjUBYR7Nvp2Y/uLV1G9UWLHOB87Jlyyx6IxDW0czbqDvfIxsYyvXkC99nJ1ceGOkK+TeBgFfFiVkPbHIneeyxx0TIULvj33bbbWLoGQ85c2ThwoXiuUceeUT4P3CRlhwrrzcCYR3Nvp2a/eAWCNtoIKyjmbfRtnyPXMzLnwdHkRi+ZnfXQMUr4iRQdiiuCr/kkkvEbbmOPLeAK+avvvpq1ZhL5hHZIIjXn3vvueWUz7D1PkLdzOsYCNtpIKyjmbfRQFjHQNhGPf0eGXZ55cgPi6+77rpLfI9sy89/Z5R5OLqNnJhlh2JFz3bkXD+gtf1lRc9eFnL55Try4zx+mocuSXhGA/99cHCw8MHYvHmzRU8EwjqafTs1+zoGwjYaCOto5m20rd+jLPJl8fGHP/xBONomJycL23o2igtkWiVOzLpDsTsfq3GuhObCKvbq4D56uY45OTmWLl26CAMgx4KltLQ0u2LIbdu2ic4NrQ+GHgiEdTT7dmr2dQyEbTQQ1tHM26i3v0cWXvw+PDzz448/9tPaGFicmHmH4o2D1Tqree08Ai7SktXTbEnOEx8jIyPVcKIMt3HnBk+B1DOBsI5m307Nvo6BsI0GwjqaeRttr+9x/fr1Pl8HU4iTQNihuFXru+++syvEYjty3inkevC6s8UwT6aVo+G5bY2d/jgnqnfMvo6BsJ2afR3Nvo0GwjqafRsNlO/RMJETs38R2sIs2arGfec33nij3eu4crxnz56isvriiy8WFstnnHGGnSGXXgmEdTT7dmr2dQyEbTQQ1tHM22ggfY/+ogP/466bLNvohoaGitts2c3W5FdccQVFR0cLe10J2+xOnDiR6uvracSIEfTrr79S37596aOPPqLU1FQyEuPGjaMbb7xR2AxLm3Je7z179tCGDRuEZfKQIUPE80bFbOsYCNtpIKyjmbfRQFjHQNtGzfo9+o22qhtWvXICJCtHqR53794tCnu4T1s+bzT27t1rSU1NtcsFavOiZiAQ1tHs26nZ1zEQttFAWEczb6OB9j36gjaJE7N+ETLkyH3n2dnZ6uMckrzlllssBQUFFqMTCOto9u3U7OsYCNtoIKyjmbfRQPwedT2VWGaCVq5cSTExMXTKKaeoExTvvvtuOnbsGBmZDh06iOu1a9fSRRddRIsXL6bu3bvTq6++ShdccAGlpKSQ0QmEdTT7dmr2dQyEbTQQ1tHM22ggfY8+py3K5vbbb7c8+OCDqg0xt4x9//33FjPAMwy4gIkNf8LDwy3PPPOMxWwEwjqafTs1+zoGwjYaCOto5m000L5HX9FqcRIIX8SUKVMst956q6mHLZl9HQNhOzX7Opp9Gw2EdTT7Nhoo36Nuu3UcmTp1KvXq1YteeOEFioiIILPR0NBAwcHBZGYCYR3Nvp2afR0DYRsNhHU08zYaSN+jr2iTOMEXAYxAIGyngbCOwNhgGwU+EycAAAAAAN6mVd06AAAAAADtBcQJAAAAAHQFxAkAAAAAdAXECQAAAAB0BcQJAAAAAHQFxAkAAAAAdAXECQCgXcnKyqIXX3yx3d7/0UcfpaFDh7bb+wMAfA/ECQABzsSJE2nWrFkUSPCgtvnz5/t7MQAALoA4AQA0C/s01tfX+3sxAAABBMQJAAHMNddcQ8uWLaOXXnpJRBP4MnfuXHH93XffifH24eHhYtz93r176fzzz6fU1FSKiYmhkSNH0o8//mj3fseOHaNzzz2XIiMjxcj4Dz/8sMn/WVJSQjfccAN16tSJ4uLi6IwzzqDff//d7WV+5plnxDLExsbS9ddfT9XV1XbPr1u3Tsxx6dixI8XHx9OECRPot99+s0szMTzKntdT3me++uorGj58uJj90qNHD3rssccgzADwAxAnAAQwLEpGjx5NN954Ix05ckRcMjMzxXMPPfSQEAI7duygwYMH08mTJ+nss8+mn376iTZu3EjTp08XQuTgwYN2YicvL4+WLFlCn3/+Ob366qtCsGj5wx/+IB5j8bNhwwYhBiZPnkzFxcUtLu+nn34qakyeeuopWr9+PXXu3Fn8H1rKy8vp6quvFoJq9erVYtgcLzc/LsUL8+6774r1lfdXrFhBV111Fd199920fft2euONN4RQe/LJJ73wSQMAPMKnM5ABALpjwoQJlrvvvlu9v2TJEp63ZZk/f36LfztgwADLv//9b3E7JydH/N3atWvV53fs2CEemzNnjri/YsUKS1xcnKW6utrufbKzsy1vvPFGi//f6NGjLbfddpvdY6NGjbIMGTLE5d80NDRYYmNjLQsWLFAf42WaN2+e3esmT55seeqpp+wee//99y2dO3ducbkAAN4FkRMAgFNGjBhhd58jJ/fffz/169ePEhISRGqHoyoycsK3Q0JCRCpI0rdvX/FaCadv+H2Sk5PF38tLbm6uSBu1BP8fo0aNsnuMIz9aCgoKRCSIIyac1uHUEf+f2giPM3jZHn/8cbvlkhGlysrKFpcNAOA9Qrz4XgAAExEdHW13n4XJ4sWL6bnnnqOePXuKupKLL76Yamtr3X5PFgmcilm6dGmT57Qipi1wSqeoqEikrLp16yZqZljAtLScvGxcY3LhhRc2eY5rUAAAvgPiBIAAJywsjBoaGlp83S+//CJqSriQVB7M9+/fbxcl4eJRriPhYlkmJydHFMBKuL7k6NGjIsKiLUR1F47arFmzRtSGSLiuxHE5uQ6F60wYroEpLCy0e01oaGiTdeZl4+Vl4QUA8C8QJwAEOCwS+IDPQoNTGY2NjU5fx2mSL7/8UhTBcpfLww8/bPfaPn36iCLZm2++mV577TUhQNg/hSMskilTpogoxsyZM+nZZ5+l3r17U35+Pn3zzTdC9DimkhzhYlUWSPy6sWPHim6gbdu2ic4a7XK+//774jVlZWX0wAMP2C2DXGcu7OX34MhKYmIi/e1vf6MZM2ZQ165dRUQoKChIpHq2bt1KTzzxRBs+YQCAp6DmBIAAh9M1wcHB1L9/f9He66o244UXXhAH8TFjxgiBcuaZZ4pogxbugElPTxftu5weuemmmyglJUV9nkXNt99+S+PHj6drr71WiJM//vGPdODAAdEe3BKXXnqpEEUPPvigqG3hv7v11lvtXvP222/TiRMnxLJdeeWVdNddd9ktA/P888+LFBV3Jg0bNkw8xuuzcOFC+uGHH0Tk57TTTqM5c+aI1BAAwLd04KpYH/+fAAAAAAAuQeQEAAAAALoC4gQAoBsGDBhg18qrvThzmwUAmBOkdQAAuoFrSOrq6pw+Jy3rAQDmB+IEAAAAALoCaR0AAAAA6AqIEwAAAADoCogTAAAAAOgKiBMAAAAA6AqIEwAAAADoCogTAAAAAOgKiBMAAAAAkJ74fyoRMZ7WvECuAAAAAElFTkSuQmCC"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 9
  },
  {
   "cell_type": "code",
   "id": "c1428242dba77d11",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-30T14:14:51.584485Z",
     "start_time": "2025-06-30T14:14:51.578366Z"
    }
   },
   "source": [
    "# 将收盘价分为训练集和测试集（最后三个月作为测试集）\n",
    "\n",
    "# 训练集\n",
    "train_data = data['2018-01-02':'2019-10-08']\n",
    "train_data[:10]"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "trade_date\n",
       "2018-01-02    18.44\n",
       "2018-01-03    18.61\n",
       "2018-01-04    18.67\n",
       "2018-01-05    18.88\n",
       "2018-01-08    19.54\n",
       "2018-01-09    19.44\n",
       "2018-01-10    19.61\n",
       "2018-01-11    19.28\n",
       "2018-01-12    19.33\n",
       "2018-01-15    19.45\n",
       "Name: close, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 10
  },
  {
   "cell_type": "code",
   "id": "ed604c4b2eb764a5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-30T14:14:51.883049Z",
     "start_time": "2025-06-30T14:14:51.879742Z"
    }
   },
   "source": [
    "# 最后3个月作为测试集\n",
    "test_data = data['2019-10-08':]\n",
    "test_data[:10]"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "trade_date\n",
       "2019-10-08    22.33\n",
       "2019-10-09    22.23\n",
       "2019-10-10    22.39\n",
       "2019-10-11    22.94\n",
       "2019-10-14    23.09\n",
       "2019-10-15    22.69\n",
       "2019-10-16    22.56\n",
       "2019-10-17    22.52\n",
       "2019-10-18    22.00\n",
       "2019-10-21    21.85\n",
       "Name: close, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 11
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-30T14:14:52.046593Z",
     "start_time": "2025-06-30T14:14:52.042346Z"
    }
   },
   "cell_type": "code",
   "source": "train_data.values",
   "id": "ed89135ba4509573",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([18.44, 18.61, 18.67, 18.88, 19.54, 19.44, 19.61, 19.28, 19.33,\n",
       "       19.45, 20.25, 20.94, 21.41, 21.29, 21.2 , 21.21, 22.92, 22.33,\n",
       "       22.34, 22.11, 21.63, 21.29, 21.5 , 21.52, 21.79, 20.55, 19.55,\n",
       "       19.24, 17.35, 17.33, 17.62, 17.63, 18.01, 18.19, 18.65, 18.25,\n",
       "       18.11, 18.13, 17.83, 17.87, 18.42, 18.27, 18.39, 18.86, 19.3 ,\n",
       "       18.95, 18.7 , 18.8 , 18.34, 18.54, 18.68, 18.5 , 18.76, 17.7 ,\n",
       "       18.02, 18.32, 17.86, 18.31, 18.58, 18.93, 19.21, 19.01, 19.34,\n",
       "       19.74, 19.45, 19.11, 18.92, 18.39, 18.41, 18.79, 19.01, 18.28,\n",
       "       18.53, 19.24, 19.14, 18.74, 18.99, 19.14, 19.59, 19.37, 19.63,\n",
       "       19.94, 19.75, 19.84, 19.49, 19.87, 19.82, 19.52, 19.25, 19.73,\n",
       "       19.81, 19.7 , 19.51, 19.37, 19.06, 19.25, 18.87, 18.12, 18.64,\n",
       "       18.57, 18.69, 18.77, 18.7 , 19.  , 18.59, 18.7 , 18.82, 18.55,\n",
       "       18.67, 18.43, 17.25, 17.08, 16.28, 16.54, 16.16, 16.36, 16.04,\n",
       "       16.16, 16.57, 16.04, 16.32, 16.09, 16.02, 16.17, 16.51, 16.52,\n",
       "       16.08, 16.58, 16.6 , 16.52, 16.33, 16.19, 16.25, 16.83, 17.14,\n",
       "       17.32, 17.27, 16.92, 16.84, 16.97, 16.86, 16.56, 16.12, 16.02,\n",
       "       15.91, 16.38, 16.03, 16.38, 16.37, 16.21, 16.19, 15.87, 15.95,\n",
       "       15.75, 16.07, 16.16, 16.17, 16.28, 16.  , 16.33, 16.21, 16.07,\n",
       "       15.94, 15.98, 16.09, 16.27, 15.92, 15.86, 15.89, 15.42, 15.52,\n",
       "       15.52, 15.71, 15.68, 15.59, 15.9 , 16.13, 16.14, 16.68, 16.41,\n",
       "       16.56, 16.53, 16.69, 15.96, 16.04, 16.15, 15.17, 15.21, 15.02,\n",
       "       14.9 , 15.09, 14.84, 15.38, 16.92, 16.71, 16.93, 17.35, 16.88,\n",
       "       16.65, 17.3 , 17.16, 17.36, 17.79, 17.71, 17.71, 17.3 , 16.97,\n",
       "       16.92, 17.13, 17.39, 17.05, 17.4 , 17.58, 18.07, 17.37, 17.14,\n",
       "       17.04, 16.6 , 16.57, 16.71, 17.01, 16.63, 16.87, 17.44, 17.6 ,\n",
       "       17.45, 17.01, 17.05, 16.77, 16.88, 16.97, 17.18, 16.85, 16.98,\n",
       "       16.65, 16.42, 16.5 , 16.1 , 16.01, 16.8 , 16.97, 17.  , 17.6 ,\n",
       "       17.64, 17.56, 17.88, 17.84, 17.47, 17.52, 17.89, 18.04, 18.  ,\n",
       "       18.1 , 17.94, 18.26, 18.58, 18.86, 18.97, 19.49, 19.3 , 18.64,\n",
       "       19.82, 19.78, 20.08, 20.39, 22.43, 24.67, 24.5 , 24.05, 23.51,\n",
       "       24.46, 24.49, 24.8 , 26.7 , 26.76, 24.08, 24.09, 24.68, 23.92,\n",
       "       23.37, 23.81, 24.63, 24.23, 24.56, 24.9 , 24.8 , 23.7 , 22.91,\n",
       "       22.97, 22.83, 24.78, 25.28, 25.2 , 26.16, 26.  , 25.31, 25.41,\n",
       "       24.95, 24.42, 24.27, 23.8 , 24.5 , 24.36, 24.28, 24.5 , 23.79,\n",
       "       23.95, 24.18, 23.5 , 23.59, 23.02, 23.04, 20.74, 20.75, 20.27,\n",
       "       19.78, 20.64, 20.04, 19.88, 20.3 , 20.23, 19.64, 19.87, 20.23,\n",
       "       20.14, 20.05, 20.07, 20.63, 20.59, 20.55, 20.21, 20.13, 20.19,\n",
       "       20.13, 20.33, 20.07, 20.08, 21.09, 20.81, 21.01, 20.66, 20.73,\n",
       "       20.85, 21.55, 23.05, 23.67, 23.6 , 23.48, 23.29, 23.92, 23.81,\n",
       "       24.38, 24.15, 23.43, 23.6 , 23.54, 23.17, 23.23, 23.2 , 23.18,\n",
       "       23.37, 23.69, 23.43, 23.09, 22.72, 23.18, 22.8 , 22.87, 23.26,\n",
       "       23.39, 23.39, 23.17, 23.41, 23.23, 22.92, 21.71, 20.94, 20.85,\n",
       "       20.5 , 20.91, 20.79, 21.34, 21.07, 21.15, 21.2 , 21.33, 23.13,\n",
       "       22.76, 22.64, 22.71, 22.64, 22.22, 22.77, 22.42, 22.25, 22.32,\n",
       "       22.77, 22.84, 23.33, 23.83, 24.15, 24.32, 24.2 , 24.39, 24.71,\n",
       "       24.28, 23.53, 23.43, 23.59, 23.34, 22.97, 22.96, 22.86, 22.72,\n",
       "       23.05, 22.48, 22.33])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 12
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-30T14:14:52.187523Z",
     "start_time": "2025-06-30T14:14:52.185700Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 图片-->0-9\n",
    "# 样本--->标签\n",
    "# days天--->明天\n",
    "# [10, 11, 12, 13, 14, 15]\n",
    "# [10, 11, 12] -> 13\n",
    "# [11, 12, 13] -> 14\n",
    "# [12, 13, 14] -> 15\n"
   ],
   "id": "1ffc59143a357248",
   "outputs": [],
   "execution_count": 13
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-30T14:14:52.241159Z",
     "start_time": "2025-06-30T14:14:52.238185Z"
    }
   },
   "cell_type": "code",
   "source": "print(len(train_data.values))",
   "id": "5fd2904104ad7cc6",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "417\n"
     ]
    }
   ],
   "execution_count": 14
  },
  {
   "cell_type": "code",
   "id": "ead1e760c31afd50",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-30T14:14:52.317203Z",
     "start_time": "2025-06-30T14:14:52.312594Z"
    }
   },
   "source": [
    "import numpy as np\n",
    "\n",
    "# 将训练集划分为特征和标签，每days天一个标签\n",
    "train_data_values = train_data.values\n",
    "\n",
    "days = 3\n",
    "# range(0, 10)\n",
    "\n",
    "train_data_x = []\n",
    "train_data_y = []\n",
    "for i in range(days, len(train_data_values)):  # range(3, len(train_values))的意思是从第3天开始，到训练集的最后一天，依次取值\n",
    "    x = train_data_values[i - days:i]\n",
    "    y = train_data_values[i]\n",
    "    train_data_x.append(x)\n",
    "    train_data_y.append(y)\n",
    "\n",
    "train_data_x = np.array(train_data_x)\n",
    "train_data_y = np.array(train_data_y)\n",
    "\n",
    "# train_data_x[:5], train_data_y[:5]\n",
    "\n",
    "\n",
    "# MaxMin归一化\n",
    "train_data_normalized_x = (train_data_x - train_data_x.min()) / (train_data_x.max() - train_data_x.min())\n",
    "train_data_normalized_y = (train_data_y - train_data_y.min()) / (train_data_y.max() - train_data_y.min())\n",
    "\n",
    "train_data_normalized_x[:2], train_data_normalized_y[:2]"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[0.30201342, 0.31627517, 0.32130872],\n",
       "        [0.31627517, 0.32130872, 0.33892617]]),\n",
       " array([0.33892617, 0.3942953 ]))"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 15
  },
  {
   "cell_type": "code",
   "id": "7cbd7c801ee5875",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-30T14:14:52.365216Z",
     "start_time": "2025-06-30T14:14:52.353610Z"
    }
   },
   "source": [
    "from torch.utils.data import TensorDataset, DataLoader\n",
    "import torch\n",
    "\n",
    "train_dataset = TensorDataset(torch.tensor(train_data_normalized_x).float(),\n",
    "                              torch.tensor(train_data_normalized_y).float())\n",
    "train_loader = DataLoader(train_dataset, batch_size=2, shuffle=False)\n",
    "for x, y in train_loader:\n",
    "    print(x)\n",
    "    print(y)\n",
    "    break"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[0.3020, 0.3163, 0.3213],\n",
      "        [0.3163, 0.3213, 0.3389]])\n",
      "tensor([0.3389, 0.3943])\n"
     ]
    }
   ],
   "execution_count": 16
  },
  {
   "cell_type": "code",
   "id": "113ac50da821825e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-30T14:14:52.415297Z",
     "start_time": "2025-06-30T14:14:52.410713Z"
    }
   },
   "source": [
    "# 定义模型\n",
    "class Net(torch.nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Net, self).__init__()\n",
    "        self.fc1 = torch.nn.Linear(days, 256)\n",
    "        self.fc2 = torch.nn.Linear(256, 256)\n",
    "        self.fc3 = torch.nn.Linear(256, 256)\n",
    "        self.fc4 = torch.nn.Linear(256, 256)\n",
    "        self.fc5 = torch.nn.Linear(256, 256)\n",
    "        self.fc6 = torch.nn.Linear(256, 256)\n",
    "        self.fc7 = torch.nn.Linear(256, 1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = torch.relu(self.fc1(x))\n",
    "        x = torch.relu(self.fc2(x))\n",
    "        x = torch.relu(self.fc3(x))\n",
    "        x = torch.relu(self.fc4(x))\n",
    "        x = torch.relu(self.fc5(x))\n",
    "        x = torch.relu(self.fc6(x))\n",
    "        x = self.fc7(x)\n",
    "        return x"
   ],
   "outputs": [],
   "execution_count": 17
  },
  {
   "cell_type": "code",
   "id": "c7d627ae706aca3c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-30T14:15:07.059554Z",
     "start_time": "2025-06-30T14:14:52.448567Z"
    }
   },
   "source": [
    "# 开始训练\n",
    "model = Net()\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr=0.01)\n",
    "criterion = torch.nn.MSELoss()\n",
    "\n",
    "epochs = 110\n",
    "for epoch in range(epochs):\n",
    "    for i, (x, y) in enumerate(train_loader):\n",
    "        y_pred = model(x)\n",
    "        loss = criterion(y_pred, y.view(-1, 1))\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        if i % 100 == 0:\n",
    "            print('Epoch: {}, Loss: {:.4f}'.format(epoch, loss.item()))"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0, Loss: 0.1220\n",
      "Epoch: 0, Loss: 0.0060\n",
      "Epoch: 0, Loss: 0.0479\n",
      "Epoch: 1, Loss: 0.0611\n",
      "Epoch: 1, Loss: 0.0022\n",
      "Epoch: 1, Loss: 0.0452\n",
      "Epoch: 2, Loss: 0.0610\n",
      "Epoch: 2, Loss: 0.0024\n",
      "Epoch: 2, Loss: 0.0432\n",
      "Epoch: 3, Loss: 0.0599\n",
      "Epoch: 3, Loss: 0.0027\n",
      "Epoch: 3, Loss: 0.0408\n",
      "Epoch: 4, Loss: 0.0585\n",
      "Epoch: 4, Loss: 0.0030\n",
      "Epoch: 4, Loss: 0.0378\n",
      "Epoch: 5, Loss: 0.0563\n",
      "Epoch: 5, Loss: 0.0035\n",
      "Epoch: 5, Loss: 0.0339\n",
      "Epoch: 6, Loss: 0.0530\n",
      "Epoch: 6, Loss: 0.0042\n",
      "Epoch: 6, Loss: 0.0290\n",
      "Epoch: 7, Loss: 0.0478\n",
      "Epoch: 7, Loss: 0.0052\n",
      "Epoch: 7, Loss: 0.0238\n",
      "Epoch: 8, Loss: 0.0402\n",
      "Epoch: 8, Loss: 0.0062\n",
      "Epoch: 8, Loss: 0.0182\n",
      "Epoch: 9, Loss: 0.0305\n",
      "Epoch: 9, Loss: 0.0068\n",
      "Epoch: 9, Loss: 0.0130\n",
      "Epoch: 10, Loss: 0.0203\n",
      "Epoch: 10, Loss: 0.0069\n",
      "Epoch: 10, Loss: 0.0088\n",
      "Epoch: 11, Loss: 0.0119\n",
      "Epoch: 11, Loss: 0.0064\n",
      "Epoch: 11, Loss: 0.0057\n",
      "Epoch: 12, Loss: 0.0064\n",
      "Epoch: 12, Loss: 0.0056\n",
      "Epoch: 12, Loss: 0.0038\n",
      "Epoch: 13, Loss: 0.0033\n",
      "Epoch: 13, Loss: 0.0048\n",
      "Epoch: 13, Loss: 0.0028\n",
      "Epoch: 14, Loss: 0.0018\n",
      "Epoch: 14, Loss: 0.0040\n",
      "Epoch: 14, Loss: 0.0022\n",
      "Epoch: 15, Loss: 0.0010\n",
      "Epoch: 15, Loss: 0.0034\n",
      "Epoch: 15, Loss: 0.0019\n",
      "Epoch: 16, Loss: 0.0007\n",
      "Epoch: 16, Loss: 0.0029\n",
      "Epoch: 16, Loss: 0.0017\n",
      "Epoch: 17, Loss: 0.0006\n",
      "Epoch: 17, Loss: 0.0027\n",
      "Epoch: 17, Loss: 0.0016\n",
      "Epoch: 18, Loss: 0.0005\n",
      "Epoch: 18, Loss: 0.0024\n",
      "Epoch: 18, Loss: 0.0016\n",
      "Epoch: 19, Loss: 0.0005\n",
      "Epoch: 19, Loss: 0.0023\n",
      "Epoch: 19, Loss: 0.0016\n",
      "Epoch: 20, Loss: 0.0005\n",
      "Epoch: 20, Loss: 0.0021\n",
      "Epoch: 20, Loss: 0.0015\n",
      "Epoch: 21, Loss: 0.0005\n",
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      "Epoch: 24, Loss: 0.0005\n",
      "Epoch: 24, Loss: 0.0017\n",
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      "Epoch: 107, Loss: 0.0007\n",
      "Epoch: 107, Loss: 0.0002\n",
      "Epoch: 107, Loss: 0.0015\n",
      "Epoch: 108, Loss: 0.0007\n",
      "Epoch: 108, Loss: 0.0001\n",
      "Epoch: 108, Loss: 0.0015\n",
      "Epoch: 109, Loss: 0.0007\n",
      "Epoch: 109, Loss: 0.0001\n",
      "Epoch: 109, Loss: 0.0015\n"
     ]
    }
   ],
   "execution_count": 18
  },
  {
   "cell_type": "code",
   "id": "52b4940d9232c2ba",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-30T14:15:07.088738Z",
     "start_time": "2025-06-30T14:15:07.083683Z"
    }
   },
   "source": [
    "# 预测\n",
    "with torch.no_grad():\n",
    "    y_pred_norm = model(torch.tensor(train_data_normalized_x[3]).float())\n",
    "    # 反归一化\n",
    "    y_pred = y_pred_norm.numpy() * (train_data_y.max() - train_data_y.min()) + train_data_y.min()\n",
    "\n",
    "y_pred_norm, y_pred, train_data_y[3]"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([0.4376]), array([20.05676627]), np.float64(19.61))"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 19
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-30T14:15:07.259878Z",
     "start_time": "2025-06-30T14:15:07.175566Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 画出预测结果和真实结果的曲线\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "model.eval()\n",
    "\n",
    "train_pred_result = []\n",
    "with torch.no_grad():\n",
    "    for x in train_data_normalized_x:\n",
    "        train_pred_normalized = model(torch.tensor(x).float()).numpy()\n",
    "        train_pred_result.append(train_pred_normalized * (train_data_y.max() - train_data_y.min()) + train_data_y.min())\n",
    "\n",
    "# 用3天预测第4天，那么前3天就没有预测结果，直接补0\n",
    "train_pred_result = [train_pred_result[0]] * days + train_pred_result\n",
    "\n",
    "x_plot = [i for i in range(len(train_pred_result))]\n",
    "\n",
    "print(len(train_pred_result))\n",
    "print(len(train_data.values))\n",
    "\n",
    "plt.plot(x_plot, train_data.values, label='real')\n",
    "plt.plot(x_plot, train_pred_result, label='pred')\n",
    "plt.legend()\n",
    "plt.show()"
   ],
   "id": "c3553a1a5b48db85",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "417\n",
      "417\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 20
  },
  {
   "cell_type": "code",
   "id": "31ce791793923244",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-30T14:15:07.291189Z",
     "start_time": "2025-06-30T14:15:07.285262Z"
    }
   },
   "source": [
    "import numpy as np\n",
    "\n",
    "test_data_values = test_data.values\n",
    "\n",
    "test_data_x = []\n",
    "test_data_y = []\n",
    "for i in range(days, len(test_data_values)):  # range(3, len(train_values))的意思是从第3天开始，到训练集的最后一天，依次取值\n",
    "    x = test_data_values[i - days:i]\n",
    "    y = test_data_values[i]\n",
    "    test_data_x.append(x)\n",
    "    test_data_y.append(y)\n",
    "\n",
    "test_data_x = np.array(test_data_x)\n",
    "test_data_y = np.array(test_data_y)\n",
    "\n",
    "# 用训练集同样的比例对测试集做归一化\n",
    "test_data_normalized_x = (test_data_x - train_data_x.min()) / (train_data_x.max() - train_data_x.min())\n",
    "\n",
    "test_data_normalized_x[:2]"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.6283557 , 0.61996644, 0.63338926],\n",
       "       [0.61996644, 0.63338926, 0.6795302 ]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 21
  },
  {
   "cell_type": "code",
   "id": "480d0380bed84e9d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-30T14:15:07.415355Z",
     "start_time": "2025-06-30T14:15:07.361314Z"
    }
   },
   "source": [
    "# 画出预测结果和真实结果的曲线\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 大都督周瑜（我的微信: dadudu6789）\n",
    "\n",
    "model.eval()\n",
    "test_pred_result = []\n",
    "with torch.no_grad():\n",
    "    for x in test_data_normalized_x:\n",
    "        test_pred_normalized = model(torch.tensor(x).float()).numpy()\n",
    "        test_pred_result.append(test_pred_normalized * (train_data_y.max() - train_data_y.min()) + train_data_y.min())\n",
    "\n",
    "test_pred_result = [test_pred_result[0]] * days + test_pred_result\n",
    "\n",
    "x_plot = [i for i in range(len(test_data.index))]\n",
    "\n",
    "plt.plot(x_plot, test_data.values, label='real')\n",
    "plt.plot(x_plot, test_pred_result, label='pred')\n",
    "plt.legend()\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 22
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-30T14:15:07.475269Z",
     "start_time": "2025-06-30T14:15:07.469669Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "\n",
    "data_values = data.values\n",
    "\n",
    "data_x = []\n",
    "data_y = []\n",
    "for i in range(days, len(data_values)):  # range(3, len(train_values))的意思是从第3天开始，到训练集的最后一天，依次取值\n",
    "    x = data_values[i - days:i]\n",
    "    y = data_values[i]\n",
    "    data_x.append(x)\n",
    "    data_y.append(y)\n",
    "\n",
    "data_x = np.array(data_x)\n",
    "data_y = np.array(data_y)\n",
    "\n",
    "# 用训练集同样的比例对测试集做归一化\n",
    "data_normalized_x = (data_x - train_data_x.min()) / (train_data_x.max() - train_data_x.min())\n",
    "\n",
    "data_normalized_x[:2]"
   ],
   "id": "f71ddaa5980cc2ec",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.30201342, 0.31627517, 0.32130872],\n",
       "       [0.31627517, 0.32130872, 0.33892617]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 23
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-30T14:15:07.651510Z",
     "start_time": "2025-06-30T14:15:07.552740Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 画出预测结果和真实结果的曲线\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "model.eval()\n",
    "pred_result = []\n",
    "with torch.no_grad():\n",
    "    for x in data_normalized_x:\n",
    "        pred_normalized = model(torch.tensor(x).float()).numpy()\n",
    "        print(pred_normalized)\n",
    "        pred_result.append(pred_normalized * (train_data_y.max() - train_data_y.min()) + train_data_y.min())\n",
    "\n",
    "pred_result = [pred_result[0]] * days + pred_result\n",
    "\n",
    "x_plot = [i for i in range(len(data.index))]\n",
    "\n",
    "print(pred_result)\n",
    "\n",
    "plt.plot(x_plot, data.values, label='real')\n",
    "plt.plot(x_plot, pred_result, label='pred')\n",
    "plt.legend()\n",
    "plt.show()"
   ],
   "id": "3588ebaae71c0eca",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 24
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-30T14:15:07.788470Z",
     "start_time": "2025-06-30T14:15:07.739713Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 画出预测结果和真实结果的曲线\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "model.eval()\n",
    "\n",
    "pred_result = [v for v in train_data_values]\n",
    "begin_pred_result_len = len(pred_result)\n",
    "\n",
    "with torch.no_grad():\n",
    "\n",
    "    for _ in range(50):\n",
    "        x = pred_result[-days:]\n",
    "        normalized_x = (x - train_data_x.min()) / (train_data_x.max() - train_data_x.min())\n",
    "        pred_normalized = model(torch.tensor(normalized_x).float()).numpy()[0]\n",
    "        pred = pred_normalized * (train_data_y.max() - train_data_y.min()) + train_data_y.min()\n",
    "        pred_result.append(pred)\n",
    "\n",
    "pred_result[:begin_pred_result_len] = [0] * begin_pred_result_len\n",
    "\n",
    "print(len(pred_result))\n",
    "\n",
    "x_plot = [i for i in range(len(pred_result))]\n",
    "\n",
    "print(len(x_plot))\n",
    "print(len(data.values)) # 100就会报错，因为data.values总共只有476\n",
    "\n",
    "plt.plot(x_plot, pred_result, label='pred')\n",
    "plt.plot(x_plot, data.values[:len(pred_result)], label='real')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ],
   "id": "dcc320197cff5ca5",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22.33765096664429\n",
      "22.400342240333558\n",
      "22.471003761291506\n",
      "22.53434370994568\n",
      "22.58489559650421\n",
      "22.62211376667023\n",
      "22.648534660339358\n",
      "22.66674729347229\n",
      "22.67909059047699\n",
      "22.687341480255128\n",
      "22.692776708602906\n",
      "22.696323461532593\n",
      "22.69861975669861\n",
      "22.700097570419313\n",
      "22.701043939590456\n",
      "22.70164785385132\n",
      "22.702032227516177\n",
      "22.702275924682617\n",
      "22.702430810928345\n",
      "22.702528858184817\n",
      "22.702590670585632\n",
      "22.70262974739075\n",
      "22.702653903961185\n",
      "22.702669534683228\n",
      "22.702679481506348\n",
      "22.70268587589264\n",
      "22.70268942832947\n",
      "22.702691559791567\n",
      "22.702692980766297\n",
      "22.702694401741027\n",
      "22.702695112228394\n",
      "22.702695112228394\n",
      "22.702695112228394\n",
      "22.702695112228394\n",
      "22.702695112228394\n",
      "22.702695112228394\n",
      "22.702695112228394\n",
      "22.702695112228394\n",
      "22.702695112228394\n",
      "22.702695112228394\n",
      "22.702695112228394\n",
      "22.702695112228394\n",
      "22.702695112228394\n",
      "22.702695112228394\n",
      "22.702695112228394\n",
      "22.702695112228394\n",
      "22.702695112228394\n",
      "22.702695112228394\n",
      "22.702695112228394\n",
      "22.702695112228394\n",
      "467\n",
      "467\n",
      "476\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 25
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-30T14:15:07.877098Z",
     "start_time": "2025-06-30T14:15:07.874042Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 数据有微小变化，然后才没有变化\n",
    "pred_result[begin_pred_result_len:]"
   ],
   "id": "97f03e0fef21c285",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[np.float64(22.33765096664429),\n",
       " np.float64(22.400342240333558),\n",
       " np.float64(22.471003761291506),\n",
       " np.float64(22.53434370994568),\n",
       " np.float64(22.58489559650421),\n",
       " np.float64(22.62211376667023),\n",
       " np.float64(22.648534660339358),\n",
       " np.float64(22.66674729347229),\n",
       " np.float64(22.67909059047699),\n",
       " np.float64(22.687341480255128),\n",
       " np.float64(22.692776708602906),\n",
       " np.float64(22.696323461532593),\n",
       " np.float64(22.69861975669861),\n",
       " np.float64(22.700097570419313),\n",
       " np.float64(22.701043939590456),\n",
       " np.float64(22.70164785385132),\n",
       " np.float64(22.702032227516177),\n",
       " np.float64(22.702275924682617),\n",
       " np.float64(22.702430810928345),\n",
       " np.float64(22.702528858184817),\n",
       " np.float64(22.702590670585632),\n",
       " np.float64(22.70262974739075),\n",
       " np.float64(22.702653903961185),\n",
       " np.float64(22.702669534683228),\n",
       " np.float64(22.702679481506348),\n",
       " np.float64(22.70268587589264),\n",
       " np.float64(22.70268942832947),\n",
       " np.float64(22.702691559791567),\n",
       " np.float64(22.702692980766297),\n",
       " np.float64(22.702694401741027),\n",
       " np.float64(22.702695112228394),\n",
       " np.float64(22.702695112228394),\n",
       " np.float64(22.702695112228394),\n",
       " np.float64(22.702695112228394),\n",
       " np.float64(22.702695112228394),\n",
       " np.float64(22.702695112228394),\n",
       " np.float64(22.702695112228394),\n",
       " np.float64(22.702695112228394),\n",
       " np.float64(22.702695112228394),\n",
       " np.float64(22.702695112228394),\n",
       " np.float64(22.702695112228394),\n",
       " np.float64(22.702695112228394),\n",
       " np.float64(22.702695112228394),\n",
       " np.float64(22.702695112228394),\n",
       " np.float64(22.702695112228394),\n",
       " np.float64(22.702695112228394),\n",
       " np.float64(22.702695112228394),\n",
       " np.float64(22.702695112228394),\n",
       " np.float64(22.702695112228394),\n",
       " np.float64(22.702695112228394)]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 26
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "为什么最后的线是平的？\n",
    "\n",
    "正常来说，当输入给模型3个价格，比如[10,20,30]，如果模型在之前的训练过程中正好也遇到了和这3个价格差不多的情况,比如[10,20,31]，那么模型就知道接下来一天的价格大概是多少，也就说模型接收的输入如果是模型学过的，那么模型基本能预测准确，但是预测值总归会有一点误差，所以当我们接着用预测值作为输入，输入给模型时，如果模型还发现自己学过，那么它还能够基本预测准确，产生一个新的预测值，但是也会有一点误差，这样随着不断的预测，随着不断的用预测值作为输入，预测值本身是有误差的，新产生的预测值误差就会越来越大，慢慢就偏离了真实情况，导致模型在接收到输入时，发现是自己没有学到过的情况，这时模型就会输出一个“均值”，这个“均值”不一定就是所有训练数据的平均值，而是指模型学到的“均值”，这样当模型发现接收到的输入是自己没有学到过的，就会输出这个“均值”，这样线就平了。"
   ],
   "id": "72c84fabbf4f82e2"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
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